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Published online by Cambridge University Press:  05 June 2012

Gerhard Tutz
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Ludwig-Maximilians-Universität Munchen
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References

Abe, M. (1999). A generalized additive model for discrete-choice data.Journal of Business & Economic Statistics 17, 271–284.Google Scholar
Abramowitz, M. and Stegun, I. (1972). Handbook of Mathematical Functions. NewYork: Dover.Google Scholar
Agrawala, A. K. (1977). Machine Recognition of Patterns. New York: IEEE Press.Google Scholar
Agresti, A. (1986). Applying R2-type measures to ordered categorical data. Technometrics 28, 133–138.Google Scholar
Agresti, A. (1992a). Analysis of Ordinal Categorical Data. New York: Wiley.Google Scholar
gresti, A. (1992b). A survey of exact inference for contingency tables. Statistical Science 7, 131–153.CrossRefGoogle Scholar
Agresti, A. (1993a). Computing Conditional Maximum-Likehood-Estimates for Generalized Rasch Models using simple Loglinear Models with Diagonals Parameters.Scandinavian Journal of Statistics 20, 63–71.Google Scholar
Agresti, A. (1993b). Computing conditional maximum likelihood estimates for generalized Rasch models using simple loglinear models with diagonal parameters.Scandinavian Journal of Statistics 20, 63–72.Google Scholar
Agresti, A. (1993c). Distribution-free fitting of logit models with random effects for repeated categorical responses.Statistics in Medicine 12, 1969–1988.CrossRefGoogle ScholarPubMed
Agresti, A. (1993d). Distribution-Free Fitting of Logit-Models with Random Effects for Repeated Categorical Responses.Statistics in Medicine 12, 1969–1987.CrossRefGoogle ScholarPubMed
Agresti, A. (1997). A model for repeated measurements of a multivariate binary response.Journal of the American Statistical Association 92, 315–321.CrossRefGoogle Scholar
Agresti, A. (2001). Exact inference for categorical data: recent advances and continuing controversies.Statistics in Medicine 20(17-18), 2709–2722.CrossRefGoogle ScholarPubMed
Agresti, A. (2002). Categorical Data Analysis. New York: Wiley.CrossRefGoogle Scholar
Agresti, A. (2009). Analysis of Ordinal Categorical Data, 2nd Edition. New York: Wiley.Google Scholar
Agresti, A., Caffo, B., and Ohman-Strickland, P. (2004). Examples in which misspecification 0of a random effects distribution reduces efficiency, and possible remedies.Computational Statistics and Data Analysis 47, 639–653.CrossRefGoogle Scholar
Agresti, A. and Kezouh, A. (1983). Association models for multi-dimensional crossclassifications of ordinal variables.Communications in Statistics, Part A – Theory Meth. 12, 1261–1276.CrossRefGoogle Scholar
Agresti, A. and Lang, J. (1993). A proportional odds model with subject-specific effects for repeated ordered categorical responses.Biometrika 80, 527.CrossRefGoogle Scholar
Aitkin, M. (1979). A simultaneous test procedure for contingency table models.Journal of Applied Statistics 28, 233–242.CrossRefGoogle Scholar
Aitkin, M. (1980). A note on the selection of log-linear models.Biometrics 36, 173–178.CrossRefGoogle Scholar
Aitkin, M. (1999). A general maximum likelihood analysis of variance components in generalized linear models.Biometrics 55, 117–128.CrossRefGoogle ScholarPubMed
Akaike, H. (1973). In Petrov, B. and Caski, F. (Eds.), Information Theory and the Extension of the Maximum Likelihood Principle, Second International Symposium on Information Theory. Akademia Kiado.Google Scholar
Akaike, H. (1974). A new look at statistical model identification.IEEE Transactions on Automatic Control 19, 716–723.CrossRefGoogle Scholar
Albert, A. and Anderson, J. A. (1984). On the existence of maximum likelihood estimates in logistic regression models.Biometrika 71, 1–10.Google Scholar
Albert, A. and Lesaffre, E. (1986). Multiple group logistic discrimination.Computers and Mathematics with Applications 12, 209–224.CrossRefGoogle Scholar
Albert, J. H. and Chib, S. (2001). Sequential ordinal modelling with applications to survival data.Biometrics 57, 829–836.CrossRefGoogle Scholar
Amemiya, T. (1978). On two-step estimation of a multivariate logit model.Journal of Econometrics 19, 13–21.CrossRefGoogle Scholar
Amemiya, T. (1981). Qualitative response models: A survey.Journal of Economic Literature XIX, 1483–1536.Google Scholar
Ananth, C. V. and Kleinbaum, D. G. (1997). Regression models for ordinal responses: A review of methods and applications.International Journal of Epidemiology 26, 1323–1333.CrossRefGoogle ScholarPubMed
Anbari, M. E. and Mkhadri, A. (2008). Penalized regression combining the l1 norm and a correlation based penalty. Technical Report 6746, Institut National de recherche en informatiqueet en automatique.
Anderson, D. A. and Aitkin, M. (1985). Variance component models with binary response: Interviewer variability.Journal of the Royal Statistical Society Series B 47, 203–210.Google Scholar
Anderson, D. A. and Hinde, J. P. (1988). Random effects in generalized linear models and the EM algorithm.Communications in Statistics A – Theory and Methods 17, 3847–3856.CrossRefGoogle Scholar
Anderson, J. A. (1972). Separate sample logistic discrimination.Biometrika 59, 19–35.CrossRefGoogle Scholar
Anderson, J. A. (1984). Regression and ordered categorical variables.Journal of the Royal Statistical Society B 46, 1–30.Google Scholar
Anderson, J. A. and Blair, V. (1982). Penalized maximum likelihood estimation in logistic regression and discrimination.Biometrika 69, 123–136.CrossRefGoogle Scholar
Anderson, J. A. and Phillips, R. R. (1981). Regression, discrimination and measurement models for ordered categorical variables. Applied Statistics 30, 22–31.CrossRefGoogle Scholar
Aranda-Ordaz, F. J. (1983). An extension of the proportional-hazard-model for grouped data.Biometrics 39, 109–118.CrossRefGoogle ScholarPubMed
Armstrong, B. and Sloan, M. (1989). Ordinal regression models for epidemiologic data.American Journal of Epidemiology 129, 191–204.CrossRefGoogle ScholarPubMed
Atkinson, A. and Riani, M. (2000). Robust Diagnostic Regression Analysis. New York: Springer-Verlag.CrossRefGoogle Scholar
Avalos, M., Y., Grandvalet, and C., Ambroise (2007). Parsimonious additive models.Computational Statistics & Data Analysis 51(6), 2851–2870.CrossRefGoogle Scholar
Azzalini, A. (1994). Logistic regression for autocorrelated data with application to repeated measures.Biometrika 81, 767–775.CrossRefGoogle Scholar
Azzalini, A., A. W., Bowman, and W., Härdle (1989). On the use of nonparametric regression for linear models.Biometrika 76, 1–11.Google Scholar
Barla, A., G., Jurman, S., Riccadonna, S., Merler, M., Chierici, and C., Furlanello (2008). Machine learning methods for predictive proteomics.Briefings in Bioinformatics 9, 119–128.Google ScholarPubMed
Baumgarten, M., P., Seliske, and M. S., Goldberg (1989). Warning re. the use of GLIM macros for the estimation of risk ratio.American Journal of Epidemiology 130, 1065.CrossRefGoogle Scholar
Begg, C. and Gray, R. (1984). Calculation of polytomous logistic regression parameters using individualized regressions.Biometrika 71, 11–18.CrossRefGoogle Scholar
Belitz, C. and Lang, S. (2008). Simultaneous selection of variables and smoothing parameters in structured additive regression models.Computational Statistics and Data Analysis 51, 6044–6059.Google Scholar
Bell, R. (1992). Are ordinal models useful for classification?Statistics in Medicine 11(1), 133–134.CrossRefGoogle ScholarPubMed
Bellman, R. (1961). Adaptive Control Processes. Princeton University Press.CrossRefGoogle Scholar
Ben-Akiva, M. E. and Lerman, S. R. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. Cambridge, MA: MIT Press.Google Scholar
Bender, R. and Grouven, U. (1998). Using binary logistic regression models for ordinal data with non–proportional odds.Journal of Clinical Epidemiology 51, 809–816.CrossRefGoogle ScholarPubMed
Benedetti, J. K. and Brown, M. B. (1978). Strategies for the selection of loglinear models.Biometrics 34, 680–686.CrossRefGoogle Scholar
Berger, R. L. and Sidik, K. (2003). Exact unconditional tests for a 2×2 matched pairs design.Statistical Methods in Medical Research 12, 91–108.CrossRefGoogle Scholar
Bergsma, W., M., Croon, and J., Hagenaars (2009). Marginal Models. New York: Springer-Verlag.
Berkson, J. (1994). Application of the logistic function to bio-assay.Journal of the American Statistical Association 9, 357–365.Google Scholar
Besag, J., P. J., Green, D., Higdon, and K., Mengersen (1995). Bayesian computation and stochastic systems.Statistical Science 10, 3–66.CrossRefGoogle Scholar
Bhapkar, V. P. (1966). A note on the equivalence of two test criteria for hypotheses in categorical data.Journal of the American Statistical Association 61, 228–235.CrossRefGoogle Scholar
Binder, H. and Tutz, G. (2008). A comparison of methods for the fitting of generalized additive models.Statistics and Computing 18, 87–99.CrossRefGoogle Scholar
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York: Springer-Verlag.Google Scholar
Bishop, Y., S., Fienberg, and P., Holland (1975). Discrete Multivariate Analysis. Cambridge, MA: MIT Press.Google Scholar
Bishop, Y., S., Fienberg, and P., Holland (2007). Discrete Multivariate Analysis. NewYork: Springer-Verlag.Google Scholar
Blanchard, G., O., Bousquet, and P., Massart (2008). Statistical performance of support vector machines.Annals of Statistics 36(2), 489–531.CrossRefGoogle Scholar
Bliss, C. I. (1934). The method of probits.Science 79, 38–39.CrossRefGoogle ScholarPubMed
Böckenholt, U. and Dillon, W. R. (1997). Modelling within – subject dependencies in ordinal paired comparison data.Psychometrika 62, 412–434.Google Scholar
Bondell, H. D. and Reich, B. J. (2008). Simultaneous regression shrinkage, variable selection and clustering of predictors with oscar.Biometrics 64, 115–123.CrossRefGoogle ScholarPubMed
Bondell, H. D. and Reich, B. J. (2009). Simultaneous factor selection and collapsing levels inanova.Biometrics 65, 169–177.CrossRefGoogle Scholar
Bonney, G. E. (1987). Logistic regression for dependent binary observations.Biometrics 43, 951–973.CrossRefGoogle ScholarPubMed
Booth, J. G. and Hobert, J. P. (1999). Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm.Journal of the Royal Statistical Society B 61, 265–285.CrossRefGoogle Scholar
Börsch-Supan, A. (1987). Econometric Analysis of Discrete Choice, with Applications on the Demand for Housing in the U. S. and West-Germany. Berlin: Springer-Verlag.Google Scholar
Boulesteix, A., C., Strobl, T., Augustin, and M., Daumer. Evaluating microarray-based classifiers: an overview.Cancer Informatics 6, 77–97.
Boulesteix, A.-L. (2006). Maximally selected chi-squared statistics for ordinal variables.Biometrical Journal 48, 451–462.CrossRefGoogle Scholar
Boyles, R. A. (1983). On the covergence of the EMalgorithm.Journal of the Royal Statistical Society B 45, 47–50.Google Scholar
Bradley, R. A. (1976). Science, statistics, and paired comparison.Biometrics 32, 213–232.CrossRefGoogle Scholar
Bradley, R. A. (1984). Paired comparisons: Some basic procedures and examples. In Krishnaiah, P. and Sen, P. R. (Eds.), Handbook of Statistics, Volume 4, pp. 299–326. Elsevier.Google Scholar
Bradley, R. A. and Terry, M. E. (1952). Rank analysis of incomplete block designs, I: The method of pair comparisons.Biometrika 39, 324–345.Google Scholar
Braga-Neto, U. and Dougherty, E. R. (2004). Is cross-validation valid for smallsamplemicroarray classification?Bioinformatics 20, 374–380.CrossRefGoogle ScholarPubMed
Brant, R. (1990). Assessing proportionality in the proportional odds model for ordinal logistic regression.Biometrics 46, 1171–1178.CrossRefGoogle ScholarPubMed
Breiman, L.0 (1995). Better subset regression using the nonnegative garrotte.Technometrics 37, 373–384.CrossRefGoogle Scholar
Breiman, L. (1996a). Bagging predictors.Machine Learning 24, 123–140.CrossRefGoogle Scholar
Breiman, L. (1996b). Heuristics of instability and stabilisation in model selection.Annals of Statistics 24, 2350–2383.Google Scholar
Breiman, L. (1998). Arcing classifiers.Annals of Statistics 26, 801–849.Google Scholar
Breiman, L. (1999). Prediction games and arcing algorithms. Neural Computation 11, 1493–1517.CrossRefGoogle ScholarPubMed
Breiman, L. (2001a). Random forests.Machine Learning 45, 5–32.CrossRefGoogle Scholar
Breiman, L. (2001b). Statistical modelling. The two cultures.Statistical Science 16, 199–231.CrossRefGoogle Scholar
Breiman, L., J. H., Friedman, R. A, Olshen, and J. C., Stone (1984). Classification and Regression Trees. Monterey, CA: Wadsworth.
Breslow, N. E. (1984). Extra-poisson variation in log-linear models.Applied Statistics 33, 38–44.CrossRefGoogle Scholar
Breslow, N. E. and Clayton, D. G. (1993). Approximate inference in generalized linear mixed model.Journal of the American Statistical Association 88, 9–25.Google Scholar
Breslow, N. E., K., Halvorsen, R., Prentice, and C., Sabai (1978). Estimation of multiple relative risk functions in matched case-control studies.American Journal of Epidemiology 108, 299–307.CrossRefGoogle ScholarPubMed
Breslow, N. E. and Lin, X. (1995). Bias correction in generalized linear mixed models with a single component of dispersion.Biometrika 82, 81–91.CrossRefGoogle Scholar
Brezger, A. and Lang, S. (2006). Generalized additive regression based on Bayesian p-splines. Computational Statistics and Data Analysis 50, 967–991.CrossRefGoogle Scholar
Brillinger, D. R. and Preisler, M. K. (1983). Maximum likelihood estimation in a latent variable problem. In Amemiya, T., Karlin, S., and Goodman, T. (Eds.), Studies in Econometrics, Time Series, and Multivariate Statistics, pp. 31–65. New York: Academic Press.CrossRefGoogle Scholar
Brown, C. C. (1982). On a goodness-of-fit test for the logistic models based on score statistics. Communicattions in Statistics: Theory and Methods 11, 1087–1105.Google Scholar
Brown, M. B. (1976). Screening effects in multidimensional contingency tables. Journal of Applied Statistics 25, 37–46.CrossRefGoogle Scholar
Brownstone, D. and Small, K. (1989). Efficient estimation of nested logit models. Journal of Business & Economic Statistics 7, 67–74.Google Scholar
Bühlmann, P. (2006). Boosting for high-dimensional linear models. Annals of Statistics 34, 559–583.CrossRefGoogle Scholar
Bühlmann, P. and Hothorn, T. (2007). Boosting algorithms: regularization, prediction and model fitting (with discussion). Statistical Science 22, 477–505.CrossRefGoogle Scholar
Bühlmann, P. and Geer, S. (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer-VerlagNew York.CrossRefGoogle Scholar
Bühlmann, P. and Yu, B. (2003). Boosting with the L2 loss: Regression and classification. Journal of the American Statistical Association 98, 324–339.CrossRefGoogle Scholar
Buja, A., T., Hastie, and R., Tibshirani (1989). Linear smoothers and additive models. Annals of Statistics 17, 453–510.CrossRefGoogle Scholar
Buja, A., W., Stuetzle, and Y., Shen (2005). Loss functions for binary class probability estimation and classification: Structure and applications. Manuscript, Department of Statistics, University of Pennsylvania, Philadelphia.
Burnham, K. and Anderson, D. (2004). Multimodel inference – understanding AIC and BIC in model selection. Sociological Methods & Research 33, 261–304.CrossRefGoogle Scholar
Burnham, K. P. and Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information–Theoretic Approach. New York: Springer–Verlag.Google Scholar
Caffo, B., M.-W., An, and C., Rhode (2007). Flexible random intercept models for binary outcomes using mixtures of normals. Computational Statistics & Data Analysis 51, 5220–5235.CrossRefGoogle Scholar
Cameron, A. C. and Trivedi, P. K. (1998). Regression Analysis of Count Data. Econometric Society Monographs No. 30. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Campbell, G. (1980). Shrunken estimators in discriminant and canonical variate analysis. Applied Statistics 29, 5–14.CrossRefGoogle Scholar
Campbell, G. (1994). Advances in statistical methodology for the evaluation of diagnostic and laboratory tests. Statistics in Medicine 13, 499–508.CrossRefGoogle ScholarPubMed
Campbell, M. K. and Donner, A. P. (1989). Classification efficiency of multinomial logisticregression relative to ordinal logistic-regression. Journal of the American Statistical Association 84 (406), 587–591.CrossRefGoogle Scholar
Campbell, M. K., A. P., Donner, and K. M., Webster (1991). Are ordinal models useful for classification?Statistics in Medicine 10, 383–394.CrossRefGoogle ScholarPubMed
Candes, E. and Tao, T. (2007). The Dantzig selector: Statistical estimation when p is much larger than n. Annals of Statistics 35(6), 2313–2351.CrossRefGoogle Scholar
Cantoni, E., J., Flemming, and E., Ronchetti (2005). Variable selection for marginal longitudinal generalized linear models. Biometrics 61, 507–514.CrossRefGoogle ScholarPubMed
Carroll, R., S., Wang, and C., Wang (1995). Prospective Analysis of Logistic Case-Control Studies. Journal of the American Statistical Association 90(429).CrossRefGoogle Scholar
Carroll, R. J., J., Fan, I., Gijbels, and M. P., Wand (1997). Generalized partially linear singleindex models. Journal of the American Statistical Association 92, 477–489.CrossRefGoogle Scholar
Carroll, R. J. and Pederson, S. (1993). On robustness in the logistic regression model. Journal of the Royal Statistical Society B 55, 693–706.Google Scholar
Carroll, R. J., D., Ruppert, and A. H., Welsh (1998). Local estimating equations. Journal of the American Statistical Association 93, 214–227.CrossRefGoogle Scholar
Celeux, G. and Diebolt, J. (1985). The SEM algorithm: A probabilistic teacher algorithm derived fom EM algorithm for the mixture Problem. Computational. Statistics 2, 73–82.Google Scholar
Chaganty, N. and Joe, H. (2004). Efficiency of generalized estimation equations for binary responses. Journal of the Royal Statistical Society B 66, 851–860.CrossRefGoogle Scholar
Chambers, J. M. and Hastie, T. J. (1992). Statistical Models in S. Pacific Grove, CA: Wadsworth Brooks/Cole.Google Scholar
Chen, J. and Davidian, M. (2002). A monte carlo EM algorithm for generalized linear models with flexible random effects distribution. Biostatistics 3, 347–360.CrossRefGoogle ScholarPubMed
Chen, S. S., D. L., Donoho, and M. A., Saunders (2001). Atomic decomposition by basis pursuit. Siam Review 43(1), 129–159.CrossRefGoogle Scholar
Chib, S. and Carlin, B. (1999). On mcmc sampling in hierarchical longitudinal models. Statistics and Computing 9, 17–26.CrossRefGoogle Scholar
Christensen, R. (1997). Log-linear Models and Logistic Regression. New York: Springer-Verlag.Google Scholar
Christmann, A. and Rousseeuw, P. J. (2001). Measuring overlap in binary regression. Computational Statistics and Data Analysis 37, 65–75.CrossRefGoogle Scholar
Chu, W. and Keerthi, S. (2005). New approaches to support vector ordinal regression. In Proceedings of the 22nd international conference on Machine learning, pp. 145–152. ACM.Google Scholar
Ciampi, A., C.-H., Chang, S., Hogg, and S., McKinney (1987). Recursive partitioning: A versatile method for exploratory data analysis in biostatistics. In McNeil, I. and Umphrey, G. (Eds.), Biostatistics. New York: D. Reidel Publishing.Google Scholar
Claeskens, G. and Hart, J. D. (2009). Goodness-of-fit tests in mixed models. TEST 18, 213–239.CrossRefGoogle Scholar
Claeskens, G. and Hjort, N. (2008). Model Selection and Model Averaging. Cambridge University Press.CrossRefGoogle Scholar
Claeskens, G., T., Krivobokova, and J. D., Opsomer (2009). Asymptotic properties of penalized spline estimators. Biometrika 96(3), 529–544.CrossRefGoogle Scholar
Clark, L. and Pregibon, D. (1992). Tree-based models. In Chambers, J. and Hastie, T. (Eds.), Statistical Models in S, pp. 377–420. Pacific Grove, California: Wadsworth & Brooks.Google Scholar
Cleveland, W. S. and Loader, C. (1996). Smoothing by local regression: Principles and methods. In Härdle, W. and Schimek, M. (Eds.), Statistical Theory and Computational Aspects of Smoothing, pp. 10–49. Heidelberg: Physica-Verlag.CrossRefGoogle Scholar
Cochran, W. (1954). Some methods for strengthening the common χ 2 tests. Biometrics 10, 417–451.CrossRefGoogle Scholar
Colonius, H. (1980). Representation and uniquness of the Bradley-Terry-Luce model for paired comparisons. British Journal of Mathematical & Statistical Psychology 33, 99–103.CrossRefGoogle Scholar
Conaway, M. R. (1989). Analysis of repeated categorical measurements with conditional likelihood methods. Journal of the American Statistical Association 84, 53–62.CrossRefGoogle Scholar
Conolly, M. A. and Liang, K. Y. (1988). Conditional logistic regression models for correlated binary data. Biometrika 75, 501–506.CrossRefGoogle Scholar
Consul, P. C. (1998). Generalized Poisson distributions. New York: Marcel Dekker.Google Scholar
Cooil, B. and Rust, R. T. (1994). Reliability and Expected Loss: A Unifying Principle. Psychometrika 59, 203–216.CrossRefGoogle Scholar
Cook, R. D. (1977). Detection of influential observations in linear regression. Technometrics 19, 15–18.Google Scholar
Cook, R. D. and Weisberg, S. (1982). Residuals and Influence in Regression. London: Chapman & Hall.Google Scholar
Copas, J. B. (1988). Binary regression models for contaminated data (with discussion). Journal of the Royal Statistical Society B 50, 225–265.Google Scholar
Cordeiro, G. and McCullagh, P. (1991). Bias correction in generalized linear models. Journal of the Royal Statistical Society. Series B (Methodological) 53, 629–643.Google Scholar
Cornell, R. G. and Speckman, J. A. (1967). Estimation for a simple exponential model. Biometrics 23, 717–737.CrossRefGoogle ScholarPubMed
Coste, J., E., Walter, D., Wasserman, and A., Venot (1997). Optimal discriminant analysis for ordinal responses. Statistics in Medicine 16(5), 561–569.3.0.CO;2-C>CrossRefGoogle ScholarPubMed
Cover, T. M. and Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 21–27.CrossRefGoogle Scholar
Cox, C. (1995). Location-scale cumulative odds models for ordinal data: A generalized nonlinear model approach. Statistics in Medicine 14, 1191–1203.CrossRefGoogle Scholar
Cox, D. (1958). Two further applications of a model for binary regression. Biometrika 45, 562–565.CrossRefGoogle Scholar
Cox, D. R. and Hinkley, D. V. (1974). Theoretical Statistics. London: Chapman & Hall.CrossRefGoogle Scholar
Cox, D. R. and Reid, N. (1987). Approximations to noncentral distributions. Canadian Journal of Statistics 15, 105–114.CrossRefGoogle Scholar
Cox, D. R. and Snell, E. J. (1989). Analysis of Binary Data (Second Edition). London; New York: Chapman & Hall.Google Scholar
Cox, D. W. J. and Wermuth, N. (1992). A comment on the coefficient of determination for binary responses. American Statistician 46, 1–4.Google Scholar
Cramer, J. S. (1991). The Logit Model. New York: Routhedge, Chapman & Hall.Google Scholar
Cramer, J. S. (2003). The origins and development of the logit model. Manuscript, University of Amsterdam and Tinbergen Institute.CrossRef
Creel, M. D. and Loomis, J. B. (1990). Theoretical and empirical advantages of truncated count data estimators for analysis of deer hunting in California. Journal of Agricultural Economics 72, 434–441.CrossRefGoogle Scholar
Crowder, M. (1995). On the use of working correlation matrix in using generalized linear models for repeated measuresh. Biometrika 82, 407–410.CrossRefGoogle Scholar
Crowder, M. J. (1987). Beta-binomial ANOVA for proportions. Journal of the Royal Statistical Society 27, 34–37.Google Scholar
Currie, I., M., Durban, and H. P., Eilers (2004). Smoothing and forecasting mortality rates. Statistical Modelling 4, 279–298.CrossRefGoogle Scholar
Czado, C. (1992). On link selection in generalized linear models. In in Statistics, S. L. N. (Ed.), Advances in GLIM and Statistical Modelling. New York: Springer–Verlag. 78, 60–65.CrossRefGoogle Scholar
Czado, C. (1997). On selecting parametric link transformation families in generalized linear models. Journal of Statistical Planning and Inference 61(1), 125–139.CrossRefGoogle Scholar
Czado, C., V., Erhardt, A., Min, and S., Wagner (2007). Zero-inflated generalized poisson models with regression effects on the mean. dispersion and zero-inflation level applied to patent outsourcing rates. Statistical Modelling 7(2), 125–153.CrossRefGoogle Scholar
Czado, C. and Munk, A. (2000). Noncanonical links in generalized linear models – When is the effort justified?Journal of Statistical Planning and Inference 87(2), 317–345.CrossRefGoogle Scholar
Czado, C. and Santner, T. (1992). The effect of link misspecification on binary regression inference. Journal of Statistical Planning and Inference 33(2), 213–231.CrossRefGoogle Scholar
Dahinden, C., G., Parmigiani, M. C., Emerick, and P., Bühlmann (2007). Penalized likelihood for sparse contingency tables with application to full-length cDNA libraries. BMC Bioinformatics 8, 476.CrossRefGoogle ScholarPubMed
Dale, J. R. (1986). Global cross-ratio models for bivariate, discrete, ordered responses. Biometrics 42, 909–917.CrossRefGoogle ScholarPubMed
Darroch, J. N., S. L., Lauritzen, and T. P., Speed (1980). Markov fields and log-linear interaction models for contingency tables. Annals of Statistics 8(3), 522–539.CrossRefGoogle Scholar
Davidson, R. (1970). On extending the Bradley-Terry model to accommodate ties in paired comparison experiments. Journal of the American Statistical Association 65, 317–328.CrossRefGoogle Scholar
Davis, C. S. (1991). Semi-parametric and non-parametric methods for the analysis of repeated measurements with applications to clinical trials. Statistics in Medicine 10, 1959–1980.CrossRefGoogle ScholarPubMed
Day, N. and Kerridge, D. (1967). A general maximum likelihood discriminant. Biometrics 23, 313–323.CrossRefGoogle ScholarPubMed
Daye, Z. and Jeng, X. (2009). Shrinkage and model selection weighted fusion. Computational Statistics and Data Analysis 53, 1284–1298.CrossRefGoogle Scholar
De Boeck, P. and Wilson, M. (2004). Explanatory item response models: A generalized linear and nonlinear approach. Springer Verlag.CrossRefGoogle Scholar
De Bruijn, N. G. (1981). Asymptotic Methods in Analysis. Dover.Google Scholar
Dean, C., J. F., Lawless, and G. E., Willmot (1989). A mixed poisson-inverse Gaussian regression model. The Canadian Journal of Statistics 17, 171–181.CrossRefGoogle Scholar
Deb, P. and Trivedi, P. K. (1997). Demand for medical care by the elderly: A finite mixture approach. Journal of Applied Econometrics 12(3), 313–336.3.0.CO;2-G>CrossRefGoogle Scholar
Delyon, B., M., Lavielle, and E., Moulines (1999). Convergence of a stochastic approximation version of the EM algorithm. Annals of Statistics 27, 94–128.Google Scholar
Deming, W. E. and Stephan, F. F. (1940). On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. Annals of Mathematical Statistics 11, 427–444.CrossRefGoogle Scholar
Demoraes, A. R. and Dunsmore, I. R. (1995). Predictive comparisons in ordinal models. Communications in Statistics – Theory and Methods 24(8), 2145–2164.CrossRefGoogle Scholar
Dempster, A. P., N. M., Laird, and D. B., Rubin (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B 39, 1–38.Google Scholar
Deng, P. and Paul, S. R. (2005). Score tests for zero-inflation and over-dispersion in generalized linear models. Statistica Sinica 15(1), 257–276.Google Scholar
Denison, D. G. T., B. K., Mallick, and A. F. M., Smith (1998). Automatic Bayesian curve fitting. Journal of the Royal Statistical Society B 60, 333–350.CrossRefGoogle Scholar
Dettling, M. and Bühlmann, P. (2003). Boosting for tumor classification with gene expression data. Bioinformatics 19, 1061–1069.CrossRefGoogle ScholarPubMed
Dey, D. K., S. K., Ghosh, and B. K., Mallick (2000). Generalized Linear Models: A Bayesian Perspective. New York: Marcel Dekker.Google Scholar
Diaz-Uriarte, R. and Andres, S. (2006a). Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7, 3.CrossRefGoogle ScholarPubMed
Diaz-Uriarte, R. and Andres, S. A. (2006b). Gene selection and classification of microarray data using random forest. Bioinformatics 7, 3.Google ScholarPubMed
Dierckx, P. (1993). Curve and Surface Fitting with Splines. Oxford: Oxford Science Publications.Google Scholar
Dietterich, T. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging boosting and randomization. Machine Learning 40(2), 139–157.CrossRefGoogle Scholar
Diggle, P. J., P. J., Heagerty, K. Y., Liang, and S. L., Zeger (2002). Analysis of Longitudinal Data (Second Edition). London: Chapman & Hall.Google Scholar
Dillon, W. R., A., Kumar, and Borrero, M. (1993). Capturing individual differences in paired comparisons: An extended BTL model incorporating descriptor variables. Journal of Marketing Research 30, 42–51.CrossRefGoogle Scholar
Dittrich, R., R., Hatzinger, and W., Katzenbeisser (1998). Modelling the effect of subject-specific covariates in paired comparison studies with an application to university rankings. Applied Statistics 47, 511–525.Google Scholar
Dobson, A. J. (1989). Introduction to Statistical Modelling. London: Chapman & Hall.Google Scholar
Dodd, L. and Pepe, M. (2003). Partial AUC estimation and regression. Biometrics 59(3), 614–623.CrossRefGoogle ScholarPubMed
Domeniconi, C., J., Peng, and D., Gunopulos (2002). Locally adaptive metric nearest-neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 1281–1285.CrossRefGoogle Scholar
Domeniconi, C. and Yan, B. (2004). Nearest neighbor ensemble. In Proc. of the 17th International Conference on Pattern Recognition, Volume 1, pp. 228–231.CrossRefGoogle Scholar
Duchon, J. (1977). Splines minimizing rotation-invariant semi-norms in solobev spaces. In Schemp, W. and Zeller, K. (Eds.), Construction Theory of Functions of Several Variables, pp. 85–100. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Dudoit, S., J., Fridlyand, and T. P., Speed (2002). Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97, 77–87.CrossRefGoogle Scholar
Duffy, D. E. and Santner, T. J. (1989). On the small sample properties of restricted maximum likelihood estimators for logistic regression models. Communication in Statistics – Theory & Methods 18, 959–989.CrossRefGoogle Scholar
Edwards, D. and Havranek, T. (1987). A fast model selection procedure for large family of models. Journal of the American Statistical Association 82, 205–213.CrossRefGoogle Scholar
Efron, B. (1975). The efficiency of logistic regression compared to normal discriminant analysis. Journal of the American Statistical Association 70, 892–898.CrossRefGoogle Scholar
Efron, B. (1978). Regression and ANOVA with zero–one data: Measures of residual variation. Journal of the American Statistical Association 73, 113–121.CrossRefGoogle Scholar
Efron, B. (1983). Estimating the error rate of a prediction rule: improvement on crossvalidation. Journal of the American Statistical Association 78(382), 316–331.CrossRefGoogle Scholar
Efron, B. (1986). Double exponential families and their use in generalized linear regression. Journal of the American Statistical Association 81, 709–721.CrossRefGoogle Scholar
Efron, B. (2004). The estimation of prediction error: Covariance penalties and cross-validation. Journal of the American Statistical Association 99, 619–632.CrossRefGoogle Scholar
Efron, B., T., HastieI., Johnstone, and R., Tibshirani (2004). Least angle regression. Annals of Statistics 32, 407–499.Google Scholar
Efron, B. and Tibshirani, R. (1997). Improvements on cross-validation: The .632+ bootstrap method. Journal of the American Statistical Association 92, 548–60.Google Scholar
Eilers, P. H. C. and Marx, B. D. (2003). Multivariate calibration with temperature interaction using two-dimensional penalized signal regression. Chemometrics and intelligent laboratory systems 66, 159–174.CrossRefGoogle Scholar
Everitt, B. and Hothorn, T. (2006). A Handbook of Statistical Analyses Using R. New York: Chapman & Hall.CrossRefGoogle Scholar
Fahrmeir, L. (1987). Asymptotic likelihood inference for nonhomogeneous observations. Statistische Hefte (N.F.) 28, 81–116.CrossRefGoogle Scholar
Fahrmeir, L. (1994). Dynamic modelling and penalized likelihood estimation for discrete time survival data. Biometrika 81(2), 317.CrossRefGoogle Scholar
Fahrmeir, L. and Frost, H. (1992). On stepwise variable selection in generalized linear regression and time series models. Computational Statistics 7, 137–154.Google Scholar
Fahrmeir, L. and Hamerle, A. (1984). Multivariate statistische Verfahren. Berlin / New York: de Gruyter.Google Scholar
Fahrmeir, L. and Kaufmann, H. (1985). Consistency and asymptotic normality of themaximum likelihood estimator in generalized linear models. Annals of Statistics 13, 342–368.CrossRefGoogle Scholar
Fahrmeir, L. and Kaufmann, H. (1987). Regression model for nonstationary categorical time series. Journal of Time Series Analysis 8, 147–160.CrossRefGoogle Scholar
Fahrmeir, L. and Kneib, T. (2009). Bayesian regularisation in structured additive regression: A unifying perspective on shrinkage, smoothing and predictor selection. Statistics and Computing 2, 203–219.Google Scholar
Fahrmeir, L. and Kneib, T. (2010). Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data. Oxford: Clarendon Press.Google Scholar
Fahrmeir, L., T., Kneib, and S., Lang (2004). Penalized structured additive regression for space-time data: a Bayesian perspective. Statistica Sinica 14, 715–745.Google Scholar
Fahrmeir, L., T., Kneib, S., Lang, and B., Marx (2011). Regression. Models, Methods and Applications. Berlin: Springer Verlag.
Fahrmeir, L. and Lang, S. (2001). Bayesian inference for generalized additive mixed models based on Markov random field priors. Applied Statistics 50(2), 201–220.Google Scholar
Fahrmeir, L. and Pritscher, L. (1996). Regression analysis of forest damage by marginal models for correlated ordinal responses. Journal of Environmental and Ecological Statistics 3, 257–268.CrossRefGoogle Scholar
Fahrmeir, L. and Tutz, G. (2001). Multivariate Statistical Modelling based on Generalized Linear Models. New York: Springer.CrossRefGoogle Scholar
Famoye, F. and Singh, K. P. (2003). On inflated generalized Poisson regression models. Advanced Applied Statistics 3(2), 145–158.Google Scholar
Famoye, F. and Singh, K. P. (2006). Zero-inflated generalized Poisson model with an application to domestic violence data. Journal of Data Science 4(1), 117–130.Google Scholar
Fan, J. and Gijbels, I. (1996). Local Polynomial Modelling and Its Applications. London: Chapman & Hall.Google Scholar
Fan, J. and Li, R. (2001). Variable selection via nonconcave penalize likelihood and its oracle properties. Journal of the American Statistical Association 96, 1348–1360.CrossRefGoogle Scholar
Fan, J. and Zhang, W. (1999). Statistical estimation in varying coefficient models. Annals of Statistics 27(5), 1491–1518.Google Scholar
Faraway, J. (2006). Extending the Linear Model with R. London: Chapman & Hall.Google Scholar
Fienberg, S. E. (1980). The Analysis of Cross-classified Categorical Data. Cambridge: MIT Press.Google Scholar
Finney, D. (1947). The estimation from individual records of the relationship between dose and quantal response. Biometrika 34, 320–334.CrossRefGoogle ScholarPubMed
Firth, D. (1987). On the efficiency of quasi-likelihood estimation. Biometrika 74, 233–245.CrossRefGoogle Scholar
Firth, D. (1991). Generalized linear models. In Hinkley, D. V.Reid, N., and Snell, E. J. (Eds.), Statistical Theory and Modelling. London: Chapman & Hall.Google Scholar
Firth, D. (1993). Bias reduction of maximum likelihood estimates. Biometrika 80(1), 27–38.CrossRefGoogle Scholar
Firth, D. and Menezes, R. (2004). Quasi-variances. Biometrika 91, 65.CrossRefGoogle Scholar
Fitzmaurice, G. M. (1995). A caveat concerning independence estimating equations with multivariate binary data. Biometrics 51, 309–317.CrossRefGoogle ScholarPubMed
Fitzmaurice, G. M. and Laird, N. M. (1993). A likelihood-based method for analysing longitudinal binary responses. Biometrika 80, 141–151.CrossRefGoogle Scholar
Fitzmaurice, G. M., N. M., Laird, and J. H., Ware (2004). Applied Longitudinal Analysis. New York: Wiley.Google Scholar
Fix, E. and Hodges, J. L. (1951). Discriminatory analysis-nonparametric discrimination: Consistency properties. US Air Force School of Aviation Medicine, Randolph Field, Texas.
Fleiss, J., B., Levin, and C., Paik (2003). Statistical Methods for Rates and Proportions. New York: Wiley.CrossRefGoogle Scholar
Flury, B. (1986). Proportionality of k covariance matrices. Statistics and Probability Letters 4, 29–33.CrossRefGoogle Scholar
Folks, J. L. and Chhikara, R. S. (1978). The inverse Gaussian distribution and its statistical application, a review (with discussion). Journal of the Royal Statistical Society B 40, 263–289.Google Scholar
Follmann, D. and Lambert, D. (1989). Generalizing logistic regression by non-parametric mixing. Journal of the American Statistical Association 84, 295–300.CrossRefGoogle Scholar
Fowlkes, E. B. (1987). Some diagnosties for binary logistic regression via smoothing. Biometrika 74, 503–515.CrossRefGoogle Scholar
Frank, E. and Hall, M. (2001). A simple approach to ordinal classification. Machine Learning: ECML 2001, 145–156.Google Scholar
Frank, I. E. and Friedman, J. H. (1993). A statistical view of some chemometrics regression tools (with discussion). Technometrics 35, 109–148.CrossRefGoogle Scholar
Freund, Y. and Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55, 119–139.CrossRefGoogle Scholar
Frühwirth-Schnatter, S. (2006). Finite mixture and Markov switching models. New York: Springer–Verlag.Google Scholar
Friedman, J., T., Hastie, and R., Tibshirani (2008). glmnet: Lasso and elastic-net regularized generalized linear models. R package version 1.1.
Friedman, J. H. (1989). Regularized discriminant analysis. Journal of the American Statistical Association 84, 165–175.CrossRefGoogle Scholar
Friedman, J. H. (1991). Multivariate adaptive regression splines (with discussion). Annals of Statistics 19, 1–67.Google Scholar
Friedman, J. H. (1994). Flexible metric nearest neighbor classification. Technical Report 113, Stanford University, Statistics Department.
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics 29, 1189–1232.CrossRefGoogle Scholar
Friedman, J. H., T., Hastie, H., Höfling, and T., Tibshirani (2007). Pathwise coordinate optimization. Applied Statistics 1(2), 302–332.CrossRefGoogle Scholar
Friedman, J. H., T., Hastie, and R., Tibshirani (2000). Additive logistic regression: A statistical view of boosting. Annals of Statistics 28, 337–407.CrossRefGoogle Scholar
Friedman, J. H., T., Hastie, and R., Tibshirani (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33(1), 1–22.CrossRefGoogle ScholarPubMed
Friedman, J. H. and Stützle, W. (1981). Projection pursuit regression. Journal of the American Statistical Association 76, 817–823.CrossRefGoogle Scholar
Fu, W. J. (1998). Penalized regression: the bridge versus the lasso. Journal of Computational and Graphical Statistics 7, 397–416.Google Scholar
Fu, W. J. (2003). Penalized estimation equations. Biometrics 59, 126–132.CrossRefGoogle Scholar
Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition. San Diego, California: Academic Press.Google Scholar
Furnival, G. M. and Wilson, R. W. (1974). Regression by leaps and bounds. Technometrics 16, 499–511.CrossRefGoogle Scholar
Gamerman, D. (1997). Efficient sampling from the posterior distribution in generalized linear mixed models. Statistics and Computing 7, 57–68.CrossRefGoogle Scholar
Gay, D. M. and Welsch, R. E. (1988). Maximum likelihood and quasi-likelihood for nonlinear exponential family regression models. Journal of the American Statistical Association 83, 990–998.CrossRefGoogle Scholar
Genkin, A., D., Lewis, and D., Madigan (2004). Large-scale Bayesian logistic regression for text categorization. Technical report, Rutgers University.
Genter, F. C. and Farewell, V. T. (1985). Goodness-of-link testing in ordinal regression models. Canadian Journal of Statistics 13, 37–44.CrossRefGoogle Scholar
Gertheiss, J. (2011). Feature Extraction in Regression and Classification with Structured Predictors. Cuvillier Verlag.Google Scholar
Gertheiss, J., S., HoggerC., Oberhauser, and G., Tutz (2011). Selection of ordinally scaled independent variables with applications to international classification of functioning core sets. Journal of the Royal Statistical Society: Series C, 377–396.CrossRefGoogle Scholar
Gertheiss, J. and Tutz, G. (2009a). Feature Selection and Weighting by Nearest Neighbor Ensembles. Chemometrics and Intelligent Laboratory Systems 99, 30–38.CrossRefGoogle Scholar
Gertheiss, J. and Tutz, G. (2009b). Penalized Regression with Ordinal Predictors. International Statistical Review 77, 345–365.CrossRefGoogle Scholar
Gertheiss, J. and Tutz, G. (2009c). Supervised feature selection in mass spectrometry based proteomic profiling by blockwise boosting. Bioinformatics 8, 1076–1077.CrossRefGoogle ScholarPubMed
Gertheiss, J. and Tutz, G. (2010). Sparse modeling of categorial explanatory variables. Annals of Applied Statistics 4, 2150–2180.CrossRefGoogle Scholar
Gertheiss, J. and Tutz, G. (2011). Regularization and model selection with categorial effect modifiers. Statistica Sinica (to appear).Google Scholar
Gijbels, I. and Verhaselt, A. (2010). P-splines regression smoothing and difference type of penalty. Statistics and Computing 4, 499–511.CrossRefGoogle Scholar
Glonek, G. F. V. and McCullagh, P. (1995). Multivariate logistic models. Journal of the Royal Statistical Society 57, 533–546.Google Scholar
Glonek, G. V. F. (1996). A class of regression models for multivariate categorical responses. Biometrika 83, 15–28.CrossRefGoogle Scholar
Gneiting, T. and Raftery, A. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association 102 (477), 359–376.CrossRefGoogle Scholar
Goeman, J. and le Cessie, S. (2006). A goodness-of-fit test for multinomial logistic regression. Biometrics 62, 980–985.CrossRefGoogle ScholarPubMed
Goeman, J. J. (2010). L1 penalized estimation in the Cox proportional hazards model. Biometrical Journal 52, 70–84.Google ScholarPubMed
Golub, T., D., Slonim, P., Tamayo, C., Huard, M., Gaasenbeek, J., Mesirov, H., Coller, M., Loh, J., Downing, M., Caligiuri, C., Bloomfield, and E., Lander (1999a). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286 (5439), 531–537.CrossRefGoogle ScholarPubMed
Golub, T. R., D. K., Slonim, P., Tamayo, C., Huard, M., Gaasenbeek, J. P., Mesirov, H., Coller, M. L., Loh, J. R., Downing, M. A., Caligiuri, C. D., Bloomfield, and E. S., Lander (1999b). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537.CrossRefGoogle ScholarPubMed
Goodman, L. A. (1968). The analysis of crossclassified data: Independence, quasiindependence and interaction in contingency tables with or without missing cells. Journal of the American Statistical Association 63, 1091–1131.Google Scholar
Goodman, L. A. (1971). The analysis of multidimensional contingency tables: Stepwise procedures and direct estimation methods for building models for multiple classifications. Technometrics 13, 33–61.CrossRefGoogle Scholar
Goodman, L. A. (1979). Simple models for the analysis of association in cross-classification having ordered categories. Journal of the American Statistical Society 74, 537–552.CrossRefGoogle Scholar
Goodman, L. A. (1981a). Association models and canonical correlation in the analysis of cross-classification having ordered categories. Journal of the American Statistical Association 76, 320–334.Google Scholar
Goodman, L. A. (1981b). Association models and the bivariate normal for contingency tables with ordered categories. Biometrika 68, 347–355.CrossRefGoogle Scholar
Goodman, L. A. (1983). The analysis of dependence in cross-classification having ordered categories, using log-linear models for frequencies and log-linear models for odds. Biometrika 39, 149–160.Google Scholar
Goodman, L. A. (1985). The analysis of cross-classified data having ordered and/or ordered categories. Annals of Statistics 13 (1), 10–69.CrossRefGoogle Scholar
Goodman, L. A. and Kruskal, W. H. (1954). Measures of associaton for cross classifications. Journal of the American Statistical Association 49, 732–764.Google Scholar
Gourieroux, C., A., Monfort, and A., Trognon (1984). Pseudo maximum likelihood methods: Theory. Econometrica 52, 681–700.CrossRefGoogle Scholar
Green, D. J. and Silverman, B. W. (1994). Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach. London: Chapman & Hall.CrossRefGoogle Scholar
Green, P. J. (1987). Penalized likelihood for general semi-parametric regression models. International Statistical Review 55, 245–259.CrossRefGoogle Scholar
Greene, W. (2003). Econometric Analysis. New Jersey: Prentice Hall.Google Scholar
Greenland, S. (1994). Alternative models for ordinal logistic regression. Statistics in Medicine 13, 1665–1677.CrossRefGoogle ScholarPubMed
Grizzle, J. E., C. F., Starmer, and G. G., Koch (1969). Analysis of categorical data by linear models. Biometrika 28, 137–156.Google ScholarPubMed
Grün, B. and F., Leisch (2007). Fitting finite mixtures of generalized linear regressions in R. Computational Statistics & Data Analysis 51(11), 5247–5252.CrossRefGoogle Scholar
Grün, B. and F., Leisch (2008). Identifiability of finite mixtures of multinomial logit models with varying and fixed effects. Journal of Classification 25(2), 225–247.CrossRefGoogle Scholar
Groll, A. and Tutz, G. (2011a). Regularization for generalized additive mixed models by likelihood-based boosting. Technical Report 110, LMU, Department of Statistics.
Groll, A. and Tutz, G. (2011b). Variable selection for generalized linear mixed models by l1-penalized estimation. Technical Report 108, LMU, Department of Statistics.
Gschoessl, S. and Czado, C. (2006). Modelling count data with overdispersion and spatial effects. Statistical Papers 49(3), 531–552.Google Scholar
Gu, C. (2002). Smoothing Splines ANOVA Models. New York: Springer–Verlag.CrossRefGoogle Scholar
Gu, C. and Wahba, G. (1991). Minimizing GCV/GML Scores with Multiple Smoothing Parameters via the Newton Method. SIAM Journal on Scientific and Statistical Computing 12(2), 383–398.CrossRefGoogle Scholar
Gu, C. and Wahba, G. (1993). Semiparametric analysis of variance with tensor product thin plate splines. Journal of the Royal Statistical Society Series B – Methodological 55(2), 353–368.Google Scholar
Guess, H. A. and Crump, K. S. (1978). Maximum likelihood estimation of dose-response functions subject to absolutely monotonic constraints. Annals of Statistics 6, 101–111.CrossRefGoogle Scholar
Guo, Y., T., Hastie, and R., Tibshirani (2007). Regularized linear discriminant analysis and its application in microarrays. Biostatistics 8(1), 86.CrossRefGoogle ScholarPubMed
Gupta, P. L., R. C., Gupta, and R. C., Tripathi (2004). Score test for zero inflated generalized Poisson regression model. Communications in Statistics – Theory and Methods 33(1), 47–64.Google Scholar
Haberman, S. J. (1974). Loglinear models for frequency tables with ordered classifications. Biometrics 30, 589–600.CrossRefGoogle Scholar
Haberman, S. J. (1977). Maximum likelihood estimates in exponential response models. Annals of Statistics 5, 815–841.CrossRefGoogle Scholar
Haberman, S. J. (1982). Analysis of dispersion of multinomial responses. Journal of the American Statistical Association 77, 568–580.CrossRefGoogle Scholar
Hamada, M. and Wu, C. F. J. (1996). A critical look at accumulation analysis and related methods. Technometrics 32, 119–130.Google Scholar
Hamerle, A. K. P. and Tutz, G. (1980). Kategoriale Reaktionen in multifaktoriellen Versuchsplänen und mehrdimensionale Zusammenhangsanalysen. Archiv für Psychologie, 53–68.Google Scholar
Hans, C. (2009). Bayesian lasso regression. Biometrika 96(1), 835–845.CrossRefGoogle Scholar
Härdle, W., P., Hall, and H., Ichimura (1993). Optimal smoothing in single-index models. Annals of Statistics 21, 157–178.CrossRefGoogle Scholar
Harrell, F. (2001). Regression Modeling Strategies. New York: Springer–Verlag.CrossRefGoogle Scholar
Hartzel, J., A., Agresti, and B., Caffo (2001). Multinomial logit random effects models. Statistical Modelling 1, 81–102.CrossRefGoogle Scholar
Hartzel, J., I., Liu, and A., Agresti (2001). Describing heterogenous effects in stratified ordinal contingency tables, with applications to multi-center clinical trials. Computational Statistics & Data Analysis 35(4), 429–449.CrossRefGoogle Scholar
Harville, D. (1997). Matrix Algebra from a Statistician's Perspective. New York: Springer–Verlag.CrossRefGoogle Scholar
Harville, D. A. (1976). Extension of the Gauss-Markov theorem to include the estimation of random effects. Annals of Statistics 4, 384–395.CrossRefGoogle Scholar
Harville, D. A. (1977). Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association 72, 320–338.CrossRefGoogle Scholar
Harville, D. A. and Mee, R. W. (1984). A mixed-model procedure for analyzing ordered categorical data. Biometrics 40, 393–408.CrossRefGoogle Scholar
Hastie, T. (1996). Pseudosplines. JRSS, Series B 58, 379–396.Google Scholar
Hastie, T. and Loader, C. (1993). Local regression: Automatic kernel carpentry. Statistical Science 8, 120–143.CrossRefGoogle Scholar
Hastie, T. and Tibshirani, R. (1986). Generalized additive models (c/r: p. 310–318). Statist. Sci. 1, 297–310.CrossRefGoogle Scholar
Hastie, T. and Tibshirani, R. (1990). Generalized Additive Models. London: Chapman & Hall.Google Scholar
Hastie, T. and Tibshirani, R. (1993). Varying-coefficient models. Journal of the Royal Statistical Society B 55, 757–796.Google Scholar
Hastie, T. and Tibshirani, R. (1996). Discriminant adaptive nearest-neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 607–616.CrossRefGoogle Scholar
Hastie, T., R., Tibshirani, and J. H., Friedman (2001). The Elements of Statistical Learning. New York: Springer-Verlag.CrossRefGoogle Scholar
Hastie, T., R., Tibshirani, and J. H., Friedman (2009). The Elements of Statistical Learning (Second Edition). New York: Springer-Verlag.CrossRefGoogle Scholar
Hausman, J. A. and McFadden, D. (1984). Specification tests for the multinomial logit model. Econometrika 52, 1219–1240.CrossRefGoogle Scholar
Hausman, J. A. and Wise, D. A. (1978). A conditional probit model for qualitative choice: Discrete decisions recognizing interdependence and heterogeneous preference. Econometrica 46, 403–426.CrossRefGoogle Scholar
He, X. and Ng, P. (1999). COBS: Qualitatively constrained smoothing via linear programming. Computational Statistics 14, 315–337.CrossRefGoogle Scholar
Heagerty, P. and Kurland, B. F. (2001). Misspecified maximum likelihood estimates and generalised linear mixed models. Biometrika 88, 973–985.CrossRefGoogle Scholar
Heagerty, P. and Zeger, S. (2000). Marginalized multilevel models and likelihood inference. Statistical Science 15(1), 1–19.Google Scholar
Heagerty, P. J. (1999). Marginally specified logistic-normal models for longitudinal binary data. Biometrics 55, 688–698.CrossRefGoogle ScholarPubMed
Heagerty, P. J. and Zeger, S. (1998). Lorelogram: A regression approach to exploring dependence in longitudinal categorical responses. Journal of the American Statistical Association 93(441), 150–162.CrossRefGoogle Scholar
Heagerty, P. J. and Zeger, S. L. (1996). Marginal regression models for clustered ordinal measurements. Journal of the American Statistical Association 91, 1024–1036.CrossRefGoogle Scholar
Hedeker, D. and Gibbons, R. B. (1994). A random-effects ordinal regression model for multilevel analysis. Biometrics 50, 933–944.CrossRefGoogle ScholarPubMed
Heim, A. (1970). Intelligence and Personality. Harmondsworth: Penguin.Google Scholar
Herbrich, R., T., Graepel, and K., Obermayer (1999). Large margin rank boundaries for ordinal regression. Advances in neural information processing systems, 115–132.Google Scholar
Heyde, C. C. (1997). Quasi-likelihood and Its Applications. New York: Springer–Verlag.CrossRefGoogle Scholar
Hilbe, J. (2011). Negative binomial regression. Cambridge University Press.CrossRefGoogle Scholar
Hinde, J. (1982). Compound poisson regression models. In Gilchrist, R. (Ed.), GLIM 1982 International Conference on Generalized Linear Models, pp. 109–121. New York: Springer-Verlag.Google Scholar
Hinde, J. and Démetrio, C. (1998). Overdispersion: Models and estimation. Computational Statistics & Data Analysis 27, 151–170.CrossRefGoogle Scholar
Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 832–844.Google Scholar
Hoaglin, D. and Welsch, R. (1978). The hat matrix in regression and ANOVA. American Statistician 32, 17–22.Google Scholar
Hoefsloot, H. C. J., S., Smit, and A. K., Smilde (2008). A classification model for the Leiden proteomics competition. Statistical Applications in Genetics and Molecular Biology 7, Article 8.Google ScholarPubMed
Hoerl, A. E. and Kennard, R. W. (1970). Ridge regression: Bias estimation for nonorthogonal problems. Technometrics 12, 55–67.CrossRefGoogle Scholar
Holtbrügge, W. and Schuhmacher, M. (1991). A comparison of regression models for the analysis of ordered categorical data. Applied Statistics 40, 249–259.CrossRefGoogle Scholar
Horowitz, J. and Härdle, W. (1996). Direct semiparametric estimation of sngle-index models with discrete covariates. Journal of the American Statistical Association 91, 1623–9.Google Scholar
Hosmer, D. H. and Lemeshow, S. (1980). Goodness-of-fit tests for the multiple logistic regression model. Communications in Statistics – Theory & Methods 9, 1043–1069.CrossRefGoogle Scholar
Hosmer, D. H. and Lemeshow, S. (1989). Applied Logistic Regression. New York: Wiley.Google Scholar
Hothorn, T., P., Bühlmann, T., Kneib, M., Schmid, and B., Hofner (2009). mboost: Model-Based Boosting. R package version 2.0-0.
Hothorn, T., K., Hornik, and A., Zeileis (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics 15, 651–674.CrossRefGoogle Scholar
Hothorn, T. and Lausen, B. (2003). On the exact distribution of maximally selected rank statistics. Computational Statistics and Data Analysis 43, 121–137.CrossRefGoogle Scholar
Hristache, M., A., Juditsky, and V., Spokoiny (2001). Direct estimation of the index coefficient in a single-index model. Annals of Statistics 29, 595–623.Google Scholar
Hsiao, C. (1986). Analysis of Panel Data. Cambridge: Cambridge University Press.Google Scholar
Huang, X. (2009). Diagnosis of random-effect model misspecification in generalized linear mixed models for binary response. Biometrics 65, 361–368.CrossRefGoogle ScholarPubMed
Huang, X., W., Pan, S., Grindle, X., Han, Y., Chen, S. J., Park, I. W., Miller, and J., Hall (2005). A comparative study of discriminating human heart failure etiology using gene expression profiles. Bioinformatics 6, 205.Google ScholarPubMed
Hurvich, C. M. and Tsai, C.-L. (1989). Regression and time series model selection in small samples. BMA 76, 297–307.Google Scholar
Ibrahim, J., H., Zhu, R., Garcia, and R., Guo (2011). Fixed and random effects selection in mixed effects models. Biometrics 67, 495–503.CrossRefGoogle ScholarPubMed
Im, S. and Gianola, D. (1988). Mixed models for bionomial data with an application to lamb mortality. Applied Statistics 37, 196–204.CrossRefGoogle Scholar
James, G. (2002). Generalized linear models with functional predictors. Journal of the Royal Statistical Society B 64, 411–432.CrossRefGoogle Scholar
James, G. M. and Radchenko, P. (2008). A generalized Dantzig selector with shrinkage tuning. Biometrika, 127–142.Google Scholar
Jank, W. (2004). Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM. Computational Statistics & Data Analysis 48, 685–701.Google Scholar
Jansen, J. (1990). On the statistical analysis of ordinal data when extravariation is present. Applied Statistics 39, 74–85.Google Scholar
Jensen, K., H., Müller, and H., Schäfer (2000). Regional confidence bands for ROC curves. Statistics in Medicine 19(4), 493–509.3.0.CO;2-W>CrossRefGoogle ScholarPubMed
Joe, H. (1989). Relative entropy measures of multivariate dependence. Journal of the American Statistical Association 84, 157–164.CrossRefGoogle Scholar
Joe, H. and Zhu, R. (2005). Generalized Poisson distribution: the property of mixture of Poisson and comparison with negative binomial distribution. Biometrical Journal 47(2), 219–229.CrossRefGoogle ScholarPubMed
Jones, R. H. (1993). Longitudinal Data with Serial Correlation: A State-Space Approach. London: Chapman & Hall.CrossRefGoogle Scholar
Jorgenson, B. (1987). Exponential dispersion models. J. Roy. Stat. Soc. Ser. B 49, 127–162.Google Scholar
Karimi, A., A., Windorfer, and J., Dreesman (1998). Vorkommen von zentralnervösen Infektionen in europäischen Ländern. Technical report, Schriften des Niedersächsischen Landesgesundheitsamtes.
Kaslow, R. A., D. G., Ostrow, R., Detels, J. P., Phair, B. F., Polk, and C. R., Rinaldo (1987). The multicenter aids cohort study: Rationale, organization and selected characteristic of the participiants. American Journal of Epidemiology 126, 310–318.CrossRefGoogle Scholar
Kauermann, G. (2000). Modelling longitudinal data with ordinal response by varying coefficients. Biometrics 56, 692–698.CrossRefGoogle Scholar
Kauermann, G., T., Krivobokova, and L., Fahrmeir (2009). Some asymptotic results on generalized penalized spline smoothing. Journal of the Royal Statistical Society Series B – Statistical Methodology 71(Part 2), 487–503.CrossRefGoogle Scholar
Kauermann, G. and Opsomer, J. (2004). Generalized cross-validation for bandwidth selection of backfitting estimates in generalized additive models. Journal of Computational Comutational and Graphical Statistics 13(1), 66–89.CrossRefGoogle Scholar
Kauermann, G. and Tutz, G. (2001). Testing generalized and semiparametric models against smooth alternatives. Journal of the Royal Statistical Society B 63, 147–166.CrossRefGoogle Scholar
Kauermann, G. and Tutz, G. (2003). Semi- and nonparametric modeling of ordinal data. Journal of Computational and Graphical Statistics 12, 176–196.CrossRefGoogle Scholar
Kaufmann, H. (1987). Regression models for nonstationary categorical time series: Asymptotic estimation theory. Annals of Statistics 15, 79–98.CrossRefGoogle Scholar
Kedem, b. and Fokianos, K. (2002). Regression Models for Time Series Analysis. NewYork: Wiley.CrossRefGoogle Scholar
Keenan, S. C. and Sobehart, J. R. (1999). Performance measures for credit risk models. Research Report 13, Moody's Risk Management Services.
Khalili, A. and Chen, J. (2007). Variable selection in finite mixture of regression models. Journal of the American Statistical Association 102(479), 1025–1038.CrossRefGoogle Scholar
Kinney, S. K. and Dunson, D. B. (2007). Fixed and random effects selection in linear and logistic models. Biometrics 63, 690–698.CrossRefGoogle ScholarPubMed
Kleiber, C. and Zeileis, A. (2008). Applied Econometrics with R. New York: Springer–Verlag.CrossRefGoogle Scholar
Klein, R. L. and Spady, R. H. (1993). An efficient semiparametric estimator for binary response models. Econometrica 61, 387–421.CrossRefGoogle Scholar
Kneib, T. and Fahrmeir, L. (2006). Structured additive regression for categorical space-time data: A mixed model approach. Biometrics 62, 109–118.CrossRefGoogle ScholarPubMed
Kneib, T. and Fahrmeir, L. (2008). A space-time study on forest health. In Chandler, R. and Scott, M. (Eds.), Statistical Methods for Trend Detection and Analysis in the Environmental Sciences. New York: Wiley.Google Scholar
Kneib, T., T., Hothorn, and G., Tutz (2009). Variable selection and model choice in geoadditive regression models. Biometrics 65, 626–634.CrossRefGoogle ScholarPubMed
Kockelkorn, U. (2000). Lineare Modelle. Oldenbourg Verlag.Google Scholar
Koenker, R., P., Ng, and S., Portnoy (1994). Quantile smoothing splines. Biometrika 81, 673–680.CrossRefGoogle Scholar
Kohavi, R. and John, G. H. (1998). The wrapper approach. In Liu, H. and Motoda, H. (Eds.), Feature Extraction, Construction and Selection. A Data Mining Perspective. Dordrecht: Kluwer.Google Scholar
Krantz, D. H. (1964). Conjoint measurement: The Luce-Tukey axiomatization and some extentions. Journal of Mathematical Psychology 1, 248–277.CrossRefGoogle Scholar
Krantz, D. H., R. D., Luce, P., Suppes, and A., Tversky (1971). Foundations of Measurement, Volume 1. New York: Academic Press.Google Scholar
Krishnapuram, B., L., Carin, M. A., Figueiredo, and A. J., Hartemink (2005). Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 957–968.CrossRefGoogle ScholarPubMed
Krivobokova, T., C., Crainiceanu, and G., Kauermann (2008). Fast adaptive penalized splines. Journal of Computational and Graphical Statistics 17, 1–20.Google Scholar
Küchenhoff, H. and Ulm, K. (1997). Comparison of statistical methods for assessing threshold limiting values in occupational epidemiology. Computational Statistics 12, 249–264.Google Scholar
Künsch, H. R., L. A., Stefanski, and R. J., Carroll (1989). Conditionally unbiased boundedinfluence estimation in general regression models, with applications to generalized linear models. Journal of the American Statistical Association 84, 460–466.Google Scholar
Kuss, O. (2002). Global goodness-of-fit tests in logistic regression with sparse data. Statistics in Medicine 21, 3789–3801.CrossRefGoogle ScholarPubMed
Laara, E. and Matthews, J. N. (1985). The equivalence of two models for ordinal data. Biometrika 72, 206–207.CrossRefGoogle Scholar
Laird, N. M., G. J., Beck, and J. H., Ware (1984). Mixed models for serial categorical response. Quoted in A. Eckholm (1991). Maximum Likelihood for Many Short Binary Time Series (preprint).
Lambert, D. (1992). Zero-inflated poisson regression with an application to defects in manufacturing. Technometrics 34, 1–14.Google Scholar
Lambert, D. and Roeder, K. (1995). Overdispersion diagnostics for generalized linear models. Journal of the American Statistical Association 90, 1225–1236.CrossRefGoogle Scholar
Land, S. R. and Friedman, J. H. (1997). Variable fusion: A new adaptive signal regression method. Discussion paper 656, Department of Statistics, Carnegie Mellon University, Pittsburg.
Landwehr, J. M., D., Pregibon, and A. C., Shoemaker (1984). Graphical methods for assessing logistic regression models. Journal of the American Statistical Association 79, 61–71.CrossRefGoogle Scholar
Lang, J. (1996a). On the comparison of multinomial and Poisson log-linear models. Journal of the Royal Statistical Society B, 253–266.Google Scholar
Lang, J. B. (1996b). Maximum likelihood methods for a generalized class of log-linear models. Annals of Statistics 24, 726–752.Google Scholar
Lang, J. B. and Agresti, A. (1994). Simultaneous modelling joint and marginal distributions of multivariate categorical responses. Journal of the American Statistical Association 89, 625–632.CrossRefGoogle Scholar
Lang, S. and Brezger, A. (2004a, MAR). Bayesian P-splines. Journal of Computational and Graphical Statistics 13(1), 183–212.CrossRefGoogle Scholar
Lang, S. and Brezger, A. (2004b). Bayesian P-splines. Journal of Computational and Graphical Statistics 13, 183–212.CrossRefGoogle Scholar
Lauritzen, S. (1996). Graphical Models. New York: Oxford University Press.Google Scholar
Lawless, J. F. and Singhal, K. (1978). Efficient screening of nonnormal regression models. Biometrics 34, 318–327.CrossRefGoogle Scholar
Lawless, J. F. and Singhal, K. (1987). ISMOD: An all-subsets regression program for generalized linear models. Computer Methods and Programs in Biomedicine 24, 117–134.CrossRefGoogle ScholarPubMed
LeCessie, (1992). Ridge estimators in logistic regression. Applied Statistics 41(1), 191–201.Google Scholar
LeCessie, S. and Houwelingen, J. C. (1991). A goodness-of-fit test for binary regression models, based on smoothing methods. Biometrics 47, 1267–1282.CrossRefGoogle Scholar
LeCessie, S. and Houwelingen, J. C. (1995). Goodness-of-fit tests for generalized linear models based on random effect models. Biometrics 51, 600–614.CrossRefGoogle Scholar
Lee, J., M., Park, and S., Song (2005). An extensive comparison of recent classification tools applied to microarray data. Computational Statistics and Data Analysis 48, 869–885.CrossRefGoogle Scholar
Lee, W.-C. and Hsiao, C. K. (1996). Alternative summary indices for the receiver operating characteristic curve. Epidemiology 7, 605–611.CrossRefGoogle ScholarPubMed
Leitenstorfer, F. and Tutz, G. (2007). Generalized monotonic regression based on B-splines with an application to air pollution data. Biostatistics 8, 654–673.CrossRefGoogle ScholarPubMed
Leitenstorfer, F. and Tutz, G. (2011). Estimation of single-index models based on boosting techniques. Statistical Modelling.CrossRefGoogle Scholar
Leng, C. (2009). A simple approach for varying-coefficient model selection. Journal of Statistical Planning and Inference 139(7), 2138–2146.CrossRefGoogle Scholar
Lesaffre, E. and Albert, A. (1989). Multiple-group logistic regression diagnostics. Applied Statistics 38, 425–440.CrossRefGoogle Scholar
Li, Y. and Ruppert, D. (2008). On the asymptotics of penalized splines. Biometrika 95, 415–436.CrossRefGoogle Scholar
Liang, K.-Y. and McCullagh, P. (1993). Case studies in binary dispersion. Biometrics 49, 623–630.CrossRefGoogle ScholarPubMed
Liang, K.-Y. and Zeger, S. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73, 13–22.CrossRefGoogle Scholar
Liang, K.-Y. and Zeger, S. (1993). Regression analysis for correlated data. Annual Reviews Public Health 14, 43–68.CrossRefGoogle ScholarPubMed
Liang, K.-Y., S., Zeger, and B., Qaqish (1992). Multivariate regression analysis for categorical data (with discussion). Journal of the Royal Statistical Society B 54, 3–40.Google Scholar
Lin, X. and Breslow, N. E. (1996). Bias correction in generalized linear mixed models with multiple components of dispersion. Journal of the American Statistical Association 91, 1007–1016.CrossRefGoogle Scholar
Lin, X. and Carroll, R. (2006). Semiparametric estimation in general repeated measures problems. Journal of the Royal Statistical Society, B, 68, 69–88.CrossRefGoogle Scholar
Lin, X. and Zhang, D. (1999). Inference in generalized additive mixed models by using smoothing splines. Journal of the Royal Statistical Society. Series B (Statistical Methodology) 61, 381–400.CrossRefGoogle Scholar
Lin, Y. and Jeon, Y. (2006). Random forests and adaptive nearest neighbors. Journal of the American Statistical Association 101, 578–590.CrossRefGoogle Scholar
Lindsey, J. J. (1993). Models for Repeated Measurements. Oxford: Oxford University Press.Google Scholar
Linton, O. B. and Härdle, W. (1996). Estimation of additive regression models with known links. Biometrika 83, 529–540.CrossRefGoogle Scholar
Lipsitz, S., G., Fitzmaurice, and G., Molenberghs (1996). Goodness-of-fit tests for ordinal response regression models. Applied Statistics 45, 175–190.CrossRefGoogle Scholar
Lipsitz, S., N., Laird, and D., Harrington (1990). Finding the design matrix for the marginal homogeneity model. Biometrika 77, 353–358.CrossRefGoogle Scholar
Lipsitz, S., N., Laird, and D., Harrington (1991). Generalized estimation equations for correlated binary data: Using the odds ratio as a measure of association in unbalanced mixed models with nested random effects. Biometrika 78, 153–160.CrossRefGoogle Scholar
Liu, Q. and Agresti, A. (2005). The analysis of ordinal categorical data: An overview and a survey of recent developments. Test 14, 1–73.Google Scholar
Liu, Q. and Pierce, D. A. (1994). A note on Gauss-Hermite quadrature. Biometrika 81, 624–629.Google Scholar
Lloyd, C. J. (2008). A new exact and more powerful unconditional test of no treatment effect from binary matched pairs. Biometrics 64(3), 716–723.CrossRefGoogle ScholarPubMed
Loader, C. (1999). Local Regression and Likelihood. New York: Springer-Verlag.Google Scholar
Loh, W. and Shih, Y. (1997). Split selection methods for classification trees. Statistica Sinica 7, 815–840.Google Scholar
Longford, N. L. (1993). Random Effect Models. New York: Oxford University Press.Google Scholar
Louis, T. A. (1982). Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society B 44, 226–233.Google Scholar
Luce, R. D. (1959). Individual Choice Behaviour. New York: Wiley.Google Scholar
Lunetta, K., L., Hayward, J., Segal, and P., Eerdewegh (2004). Screening Large-Scale Association Study Data: Exploiting Interactions Using Random Forests. BMC Genetics 5(1), 32.CrossRefGoogle ScholarPubMed
Maddala, G. S. (1983). Limited-Dependent and Qualitative Variables in Econometrics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Magder, L. and Zeger, S. (1996). A smooth nonparametric estimate of a mixing distribution using mixtures of gaussians. Journal of the American Statistical Association 91, 1141–1151.CrossRefGoogle Scholar
Magnus, J. R. and Neudecker, H. (1988). Matrix Differential Calculus with Applications in Statistics and Econometrics. London: Wiley.Google Scholar
Mancl, L. A. and Leroux, B. G. (1996). Efficiency of regression estimates for clustered data. Biometrics 52, 500–511.CrossRefGoogle ScholarPubMed
Mantel, N. and Haenszel, W. (1959). Statistical aspects of the analysis of data from retrospective studies. J. Natl. Cancer Inst. 22, 719–48.Google ScholarPubMed
Marks, S. and Dunn, O. J. (1974). Discriminant functions when covariance matrices are unequal.Journal of the American Statistical Association 69.CrossRefGoogle Scholar
Marra, G. and Wood, S. (2011). Practical variable selection for generalized additive models.Computational Statistics and Data Analysis 55, 2372–2387.CrossRefGoogle Scholar
Marx, B. D. and Eilers, P. H. C. (1998). Direct generalized additive modelling with penalized likelihood.Computational Statistics & Data Analysis 28, 193–209.CrossRefGoogle Scholar
Marx, B. D. and Eilers, P. H. C. (1999). Generalized linear regression on sampled signals and curves: A p-spline approach.Technometrics 41, 1–13.Google Scholar
Marx, B. D. and Eilers, P. H. C. (2005). Multidimensional penalized signal regression.Technometrics 47, 13–22.CrossRefGoogle Scholar
Masters, G. N. (1982). A Rasch model for partial credit scoring.Psychometrika 47, 149–174.CrossRefGoogle Scholar
McCullagh, P. (1980). Regression model for ordinal data (with discussion).Journal of the Royal Statistical Society B 42, 109–127.Google Scholar
McCullagh, P. (1983). Quasi-likelihood functions.Annals of Statistics 11, 59–67.CrossRefGoogle Scholar
McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models (Second Edition). New York: Chapman & Hall.CrossRefGoogle Scholar
McCulloch, C. and Searle, S. (2001). Generalized, Linear, and Mixed Models. NewYork: Wiley.Google Scholar
McCulloch, C. E. (1997). Maximum likelihood algorithms for generalized linear mixed models.Journal of the American Statistical Association 92, 162–170.CrossRefGoogle Scholar
McDonald, B. W. (1993). Estimating logistic regression parameters for bivariate binary data.Journal of the RoyalStatistical Society B 55, 391–397.Google Scholar
McFadden, D. (1973). Conditional logit analysis of qualitative choice behaviour. In Zarembka, P. (Ed.), Frontiers in Econometrics. New York: Academic Press.Google Scholar
McFadden, D. (1978). Modelling the choice of residential location. In Karlquist et al, A.. (Eds.), Spatial Interaction Theory and Residential Location. Amsterdam: North-Holland.Google Scholar
McFadden, D. (1981). Econometric models of probabilistic choice. In Manski, C. F. and McFadden, D. (Eds.), Structural Analysis of Discrete Data with Econometric Applications, pp. 198–272. Cambridge, MA: MIT Press.Google Scholar
McFadden, D. (1986). The choice theory approach to market research.Marketing Science 5, 275–297.CrossRefGoogle Scholar
McLachlan, G. and Krishnan, T. (1997). The EM Algorithm and Extensions. New York: Wiley.Google Scholar
McLachlan, G. J. (1992). Discriminant Analysis and Statistical Pattern Recognition. New York: Wiley.CrossRefGoogle Scholar
McLachlan, G. J. and Peel, D. (2000). Finite Mixture Models. New York: Wiley.CrossRefGoogle Scholar
McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages.Psychometrika 12, 153–157.CrossRefGoogle ScholarPubMed
Mehta, C. R., N. R., Patel, and A. A., Tsiatis (1984). Exact significance testing to establish treatment equivalence with ordered categorical data.Biometrics 40, 819–825.CrossRefGoogle ScholarPubMed
Meier, L., Geer, S., and P., Bühlmann (2008). The group lasso for logistic regression.Journal of the Royal Statistical Society, Series B 70, 53–71.CrossRefGoogle Scholar
Meier, L., Geer, S., and Bühlmann, P. (2009). High-dimensional additive modeling.The Annals of Statistics 37, 3779–3821.CrossRefGoogle Scholar
Meilijson, I. (1989). A fast improvement to the EM-algorithm on its own terms.Journal of the Royal Statistical Society B 51, 127–138.Google Scholar
Miller, A. J. (1989). Subset Selection in Regression. London: Chapman & Hall.Google Scholar
Miller, M. E., C. S., Davis, and R. J., Landis (1993). The analysis of longitudinal polytomous data: Generalized estimated equations and connections with weighted least squares.Biometrics 49, 1033–1044.CrossRefGoogle Scholar
Miller, R. and Siegmund, D. (1982). Maximally selected chi-square statistics.Biometrics 38, 1011–1016.CrossRefGoogle Scholar
Min, A. and Czado, C. (2010). Testing for zero-modification in count regression models.Statistica Sinica 20, 323–341.Google Scholar
Mittlböck, M. and Schemper, M. (1996). Explained variation for logistic regression.Statistic in Medicine 15, 1987–1997.3.0.CO;2-9>CrossRefGoogle ScholarPubMed
Mkhadri, A., G., Celeux, and A., Nasroallah (1997). Regularization in discriminant analysis: An overview.Computational Statistics & Data analysis 23, 403–423.CrossRefGoogle Scholar
Molenberghs, G. and Lesaffre, E. (1994). Marginal modelling of correlated ordinal data using a multivariate Plackett distribution.Journal of the American Statistical Association 89, 633– 644.CrossRefGoogle Scholar
Molenberghs, G. and Verbeke, G. (2005). Models for Discrete Longitdinal Data. NewYork: Springer-Verlag.Google Scholar
Molinaro, A., R., Simon, and R. M., Pfeiffer (2005). Predition error estimation: a comparison of resampling methods.Bioinformatics 21, 3301–3307.CrossRefGoogle Scholar
Moore, D. F. and Tsiatis, A. (1991). Robust estimation of the variance in moment methods for extra-binomial and extra-poisson variation.Biometrics 47, 383–401.CrossRefGoogle ScholarPubMed
Morgan, B. J. T. (1985). The cubic logistic model for quantal assay data.Applied Statistics 34, 105–113.CrossRefGoogle Scholar
Morgan, B. J. T. and Smith, D. M. (1993). A note on Wadley's problem with overdispersion.Applied Statistics 41, 349–354.Google Scholar
Morgan, J. N. and Sonquist, J. A. (1963). Problems in the analysis of survey data, and a proposal.Journal of the American Statistical Association 58, 415–435.CrossRefGoogle Scholar
Morin, R. L. and Raeside, D. E. (1981). A reappraisal of distance-weighted k-nearest neighbor classification for pattern recognition with missing data.IEEE Transactions on Systems, Man and Cybernetics 11, 241–243.Google Scholar
Moulton, L. and Zeger, S. (1989). Analysing repeated measures in generalized linear models via the bootstrap.Biometrics 45, 381–394.CrossRefGoogle Scholar
Muggeo, V. M. R. and Ferrara, G. (2008). Fitting generalized linear models with unspecified link function: A P-spline approach.Computational Statistics & Data Analysis 52(5), 2529–2537.CrossRefGoogle Scholar
Mullahy, J. (1986). Specification and testing of some modified count data models.Journal of Econometrics 33, 341–365.CrossRefGoogle Scholar
Nadaraya, E. A. (1964). On estimating regression.Theory of Probability and Applications 10, 186–190.Google Scholar
Nagelkerke, N. J. D. (1991). A note on a general definition of the coefficient of determination.Biometrika 78, 691–692.CrossRefGoogle Scholar
Naik, P. A. and Tsai, C. (2001). Single-index model selections.Biometrika 88, 821–832.CrossRefGoogle Scholar
Nair, V. N. (1987). Chi-squared-type tests for ordered alternatives in contingency tables.Journal of the American Statistical Association 82, 283–291.CrossRefGoogle Scholar
Nelder, J. A. (1992). Joint modelling of mean and dispersion. In Heijden, P., Jansen, W., Francis, B., and Seeber, G. (Eds.), Statistical Modelling. Amsterdam: North-Holland.Google Scholar
Nelder, J. A. and Pregibon, D. (1987). An extended quasi-likelihood function.Biometrika 74, 221–232.CrossRefGoogle Scholar
Nelder, J. A. and Wedderburn, R. W. M. (1972). Generalized linear models.Journal of the Royal Statistical Society A 135, 370–384.CrossRefGoogle Scholar
Neuhaus, J., W., Hauck, and J., Kalbfleisch (1992). The effects of mixture distribution. misspecification when fitting mixed effect logistic models.Biometrika 79(4), 755–762.CrossRefGoogle Scholar
Neuhaus, J. M., J. D., Kalbfleisch, and W. W., Hauck (1991). A comparison of cluster-specific and population-averaged approaches for analyzing correlated binary data.International Statistical Review 59, 25–35.CrossRefGoogle Scholar
Newson, R. (2002). Parameters behind “nonparametric” statistics: Kendall's tau, Somers' D and median differences.The Stata Journal 2, 45–64.Google Scholar
Ni, X., D., Zhang, and H. H., Zhang (2010). Variable selection for semiparametric mixed models in longitudinal studies.Biometrics 66, 79–88.CrossRefGoogle ScholarPubMed
Nyquist, H. (1991). Restricted estimation of generalized linear models.Applied Statistics 40, 133–141.CrossRefGoogle Scholar
Ogden, R. T. (1997). Essential Wavelets for Statistical Applications and Data Analysis. Boston: Birkhäuser.CrossRefGoogle Scholar
Opsomer, J. D. (2000). Asymptotic properties of backfitting estimators.Journal of Multivariate Analysis 73, 166–179.CrossRefGoogle Scholar
Opsomer, J. D. and Ruppert, D. (1997). Fitting a bivariate additive model by local polynomial regression.Annals of Statistics 25, 186–211.Google Scholar
Osborne, M., B., Presnell, and B., Turlach (2000). On the lasso and its dual.Journal of Computational and Graphical Statistics 9(2), 319–337.Google Scholar
Osborne, M. R., B., Presnell, and B. A., Turlach (1998). Knot selection for regression splines via the lasso. In Weisberg, S. (Ed.), Dimension Reduction, Computational Complexity, and Information, Volume 30 of Computing Science and Statistics, pp. 44–49.Google Scholar
Osius, G. (2004). The association between two random elements: A complete characterization and odds ratio models.Metrika 60, 261–277.CrossRefGoogle Scholar
Osius, G. and Rojek, D. (1992). Normal goodness-of-fit tests for parametric multinomial models with large degrees of freedom.Journal of the American Statistical Association 87, 1145–1152.CrossRefGoogle Scholar
Paik, M. and Yang, Y. (2004). Combining nearest neighbor classifiers versus cross-validation selection.Statistical Applications in Genetics and Molecular Biology 3(12).CrossRefGoogle ScholarPubMed
Palmgren, J. (1981). The Fisher information matrix for log-linear models arguing conditionally in the observed explanatory variables.Biometrika 68, 563–566.Google Scholar
Palmgren, J. (1989). Regression models for bivariate binary responses.UW Biostatistics Working Paper Series.Google Scholar
Park, M. Y. and Hastie, T. (2007). An l1 regularization-path algorithm for generalized linear models.Journal of the Royal Statistical Society B 69, 659–677.CrossRefGoogle Scholar
Park, T. and Casella, G. (2008). The Bayesian lasso.Journal of the American Statistical Association 103, 681–686.CrossRefGoogle Scholar
Parthasarthy, G. and Chatterji, B. N. (1990). A class of new knn methods for low sample problems.IEEE Transactions on systems, man and Cybernetics 20, 715–718.CrossRefGoogle Scholar
Patterson, H. and Thomson, R. (1971). Recovery of inter-block information when block sizes are unequal.Biometrika 58, 545–554.CrossRefGoogle Scholar
Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. New York: Chapman & Hall.Google Scholar
Peterson, B. and Harrell, F. E. (1990). Partial proportional odds models for ordinal response variables.Applied Statistics 39, 205–217.CrossRefGoogle Scholar
Petricoin, E. F., D. K., Ornstein, C. P., Paweletz, A. M., Ardekani, P. S., Hackett, B. A., Hitt, A., Velassco, C., Trucco, L., Wiegand, K., Wood, C. B., Simone, P. J., Levine, W. M., Lineham, M. R., Emmert-Buck, S. M., Steinberg, E. C., Kohn, and L. A., Liotta (2002). Serum proteomic patterns for detection of prostate cancer.Journal of the National Cancer Institute 94, 1576–1578.CrossRefGoogle ScholarPubMed
Petry, S. and Tutz, G. (2011). The oscar for generalized linear models. Technical Report 112, LMU, Department of Statistics.
Petry, S. and Tutz, G. (2012). Shrinkage and variable selection by polytopes.Journal of Statistical Planning and Inference 142, 48–64.CrossRefGoogle Scholar
Petry, S., G., Tutz, and C., Flexeder (2011). Pairwise fused lasso. Technical Report 102, LMU, Department of Statistics.
Piccarreta, R. (2008). Classification trees for ordinal variables.Computational Statistics 23(3), 407–427.CrossRefGoogle Scholar
Piegorsch, W. (1992). Complementary log regression for generalized linear models.The American Statistician 46, 94–99.Google Scholar
Piegorsch, W. W., C. R., Weinberg, and B. H., Margolin (1988). Exploring simple independent action in multifactor tables of proportions.Biometrics 44, 595–603.CrossRefGoogle ScholarPubMed
Pierce, D. A. and Schafer, D. W. (1986). Residuals in generalized linear models.Journal of the American Statistical Association 81, 977–986.CrossRefGoogle Scholar
Pigeon, J. and Heyse, J. (1999). An improved goodness-of-fit statistic for probability prediction models.Biometrical Journal 41, 71–82.3.0.CO;2-O>CrossRefGoogle Scholar
Pinheiro, J. C. and Bates, D. M. (1995). Approximations to the log-likelihood function in the nonlinear mixed-effects model.Journal of Computational and Graphical Statistics 4, 12–35.Google Scholar
Poggio, T. and Girosi, F. (1990). Regularization algorithms for learning that are equivalent to multilayer networks.Science 247, 978–982.CrossRefGoogle ScholarPubMed
Pohlmeier, W. and Ulrich, V. (1995). An econometric model of the two-part decisionmaking process in the demand for health care.Journal of Human Resources 30, 339–361.CrossRefGoogle Scholar
Poortema, K. L. (1999). On modelling overdispersion of counts.Statistica Neerlandica 53, 5–20.CrossRefGoogle Scholar
Powell, J. L., J. H., Stock, and T. M., Stoker (1989). Semiparametric estimation of index coefficients.Econometrica 57, 1403–1430.CrossRefGoogle Scholar
Pregibon, D. (1980). Goodness of link tests for generalized linear models.Applied Statistics 29, 15–24.CrossRefGoogle Scholar
Pregibon, D. (1981). Logistic regression diagnostics.Annals of Statistics 9, 705–724.CrossRefGoogle Scholar
Pregibon, D. (1982). Resistant fits for some commonly used logistic models with medical applications.Biometrics 38, 485–498.CrossRefGoogle Scholar
Pregibon, D. (1984). Review of generalized linear models by mccullagh and nelder.American Statistician 12, 1589–1596.Google Scholar
Prentice, R. and Pyke, R. (1979). Logistic disease incidence models and case-control studies.Biometrika 66, 403.CrossRefGoogle Scholar
Prentice, R. L. (1976). A generalization of the probit and logit methods for close response curves.Biometrics 32, 761–768.CrossRefGoogle Scholar
Prentice, R. L. (1986). Binary regression using an extended beta-binomial distribution, with discussion of correlation induced by covariate measurement errors. Journal of the American Statistical Association 81, 321–327.CrossRefGoogle Scholar
Prentice, R. L. (1988). Correlated binary regression with covariates specific to each binary observation. Biometrics 44, 1033–1084.CrossRefGoogle ScholarPubMed
Pulkstenis, E. and Robinson, T. J. (2002). Two goodness-of-fit tests for logistic regression models with continuous covariates. Statistics in Medicine 21, 79–93.CrossRefGoogle ScholarPubMed
Pulkstenis, E. and Robinson, T. J. (2004). Goodness-of-fit tests for ordinal response regression models. Statistics in Medicine 23, 999–1014.CrossRefGoogle ScholarPubMed
Qu, Y., G. W., Williams, G. J., Beck, and M., Goormastic (1987). A generalized model of logistic regression for clustered data. Communications in Statistics – Theory and Methods 16, 3447–3476.Google Scholar
Quinlan, J. R. (1986). Industion of decision trees. Machine Learning 1, 81–106.CrossRefGoogle Scholar
Quinlan, J. R. (1993). Programs for Machine Learning. San Francisco: Morgan Kaufmann PublisherInc.Google Scholar
,R Development Core Team (2010). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0.
Radelet, M. and Pierce, G. (1991). Choosing those who will die: Race and the death penalty in florida. Florida Law Review 43, 1–34.Google Scholar
Ramsey, J. O. and Silverman, B. W. (2005). Functional Data Analysis. New York: Springer – Verlag.CrossRefGoogle Scholar
Rao, P. and Kupper, L. (1967). Ties in paired-comparison experiments: A generalization of the Bradley-Terry model. Journal of the American Statistical Association 62, 194–204.CrossRefGoogle Scholar
Rasch, G. (1961). On general laws and the meaning of measurement in psychology. In Neyman, J. (Ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley.Google Scholar
Ravikumar, P., M., Wainwright, and J., Lafferty (2009). High-dimensional graphical model selection using l1-regularized logistic regression. Annals of Statistics 3, 1287–1319.Google Scholar
Rawlings, J., S., Pantula, and D., Dickey (1998). Applied Regression Analysis. New York: Springer–Verlag.CrossRefGoogle Scholar
Rayens, W. and Greene, T. (1991). Covariance pooling and stabilization for classification. Computational Statistics and Data Analysis 11, 17–42.CrossRefGoogle Scholar
Read, I. and Cressie, N. (1988). Goodness-of-Fit Statistics for Discrete Multivariate Data. New York: Springer-Verlag.CrossRefGoogle Scholar
Reinsch, C. (1967). Smoothing by spline functions. Numerische Mathematik 10, 177–183.CrossRefGoogle Scholar
Ridgeway, G. (1999). Generalization of boosting algorithms and applications of bayesian inference for massive datasets. Ph. D. thesis, University of Washington.
Ripley, B. D. (1996). Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press.
Robinson, G. K. (1991). That blup is a good thing. the estimation of random effects. Statistical Science 6, 15–51.CrossRefGoogle Scholar
Romualdi, C., S., Campanaro, D., Campagna, B., Celegato, N., Cannata, S., Toppo, G., Valle, and G., Lanfranchi (2003). Pattern recognition in gene expression profiling using dna array: A comparison study of different statistical methods applied to cancer classification. Human Molecular Genetics 12, 823–836.CrossRefGoogle Scholar
Rosenstone, S., D., Kinder, and W., Miller (1997). American National Election Study. MI: Inter–University Consortium for Political and Social Research.
Rosner, B. (1984). Multivariate methods in orphthalmology with applications to other paireddata situations. Biometrics 40, 1025–1035.CrossRefGoogle Scholar
Rosset, S. (2004). Tracking curved regularized optimization solution paths. In Advances in Neural Information Processing Systems, Cambridge. MIT Press.Google Scholar
Rousseeuw, P. J. and Christmann, A. (2003). Robustness against separation and outliers in logistic regression. Computational Statistics and Data Analysis 43, 315–332.CrossRefGoogle Scholar
Ruckstuhl, A. and Welsh, A. (1999). Reference bands for nonparametrically estimated link functions. Journal of Computational and Graphical Statistics 8(4), 699–714.Google Scholar
Rudolfer, S. M., P. C., Watson, and E., Lesaffre (1995). Are ordinal models useful for classification? a revised analysis. Journal of Statistical Computation Simulation 52(2), 105–132.CrossRefGoogle Scholar
Rue, H. and Held, L. (2005). Gaussian Markov Random Fields. Theory and Applications. London: CRC/Chapman & Hall.
Rumelhart, D. L. and Greeno, J. G. (1971). Similarity between stimuli: An experimental test of the Luce and restle choice methods. Journal of Mathematical Psychology 8, 370–381.CrossRefGoogle Scholar
Ruppert, D. (2002). Selecting the number of knots for penalized splines. Journal of Computational and Graphical Statistics 11, 735–757.CrossRefGoogle Scholar
Ruppert, D., M. P., Wand, and R. J., Carroll (2003). Semiparametric Regression. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Ruppert, D., M. P., Wand, and R. J., Carroll (2009). Semiparametric regression during 2003–2007. Electronic Journal of Statistics 3, 1193–1256.CrossRefGoogle ScholarPubMed
Ryan, T. (1997). Modern Regression Methods. New York: Wiley.Google Scholar
Sampson, A. and Singh, H. (2002). Min and max scorings for two sample partially ordered categorical data. Journal of statistical Planning and Inference 107, 219–236.CrossRefGoogle Scholar
Santner, T. J. and Duffy, D. E. (1986). A note on A. Albert and J. A. Anderson's conditions for the existence of maximum likelihood estimates regression models. Biometrika 73, 755–758.CrossRefGoogle Scholar
Santner, T. J. and Duffy, D. E. (1989). The Statistical Analysis of Discrete Data. NewYork: Springer-Verlag.CrossRefGoogle Scholar
Schaefer, R. L., L. D., Roi, and R. A., Wolfe (1984). A ridge logistic estimate. Communication in Statistics, Theory & Methods 13, 99–113.CrossRefGoogle Scholar
Schall, R. (1991). Estimation in generalised linear models with random effects. Biometrika 78, 719–727.CrossRefGoogle Scholar
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics 6, 461–464.CrossRefGoogle Scholar
Scott, A. and Wild, C. (1986). Fitting logistic models under case-control or choice based sampling. Journal of the Royal Statistical Society. Series B (Methodological) 48(2), 170–182.Google Scholar
Searle, S., G., Casella, and C., McCulloch (1992). Variance Components. New York: Wiley.CrossRefGoogle Scholar
Seeber, G. (1977). Linear Regression Analysis. New York: Wiley.Google Scholar
Segerstedt, B. (1992). On ordinary ridge regression in generalized linear models. Communications in Statistics – Theory and Methods 21, 2227–2246.CrossRefGoogle Scholar
Shapire, R. E. (1990). The strength of weak learnability. Machine Learning 5, 197–227.CrossRefGoogle Scholar
Shih, Y.-S. (2004). A note on split selection bias in classification trees. Computational Statistics and Data Analysis 45, 457–466.CrossRefGoogle Scholar
Shih, Y.-S. and H., Tsai (2004). Variable selection bias in regression trees with constant fits. Computational Statistics and Data Analysis 45, 595–607.CrossRefGoogle Scholar
Shipp, M., K., Ross, P., Tamayo, A., Weng, J., Kutok, R., Aguiar, M., Gaasenbeek, M., Angelo, M., Reich, G., Pinkus, et al. (2002). Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature medicine 8(1), 68–74.CrossRefGoogle ScholarPubMed
Silverman, B. W. and Jones, M. C. (1989). Commentary on Fix and Hodges (1951): An important contribution to nonparametric discriminant analysis and density estimation. International Statistical Review 57, 233–238.CrossRefGoogle Scholar
Simonoff, J. (1995). Smoothing categorical data. Journal of Statistical Planning and Inference 47, 41–69.CrossRefGoogle Scholar
Simonoff, J. S. (1983). A penalty function approach to smoothing large sparse contingency tables. Annals of Statistics 11, 208–218.CrossRefGoogle Scholar
Simonoff, J. S. (1996). Smoothing Methods in Statistics. New York: Springer-Verlag.CrossRefGoogle Scholar
Simonoff, J. S. and Tutz, G. (2000). Smoothing methods for discrete data. In Schimek, M. (Ed.), Smoothing and Regression. Approaches, Computation and Application. New York: Wiley.Google Scholar
Slawski, M. (2010). The structured elastic net for quantile regression and support vector classification. Statistics and Computing.
Slawski, M., M., Daumer, and A.-L., Boulesteix (2008). CMA – A comprehensive bioconductor package for supervised classification with high dimensional data. BMC Bioinformatics 9, 439.CrossRefGoogle ScholarPubMed
Smith, M. and Kohn, R. (1996). Nonparametric regression using Bayesian variable selection. Journal of Econometrics 75, 317–343.CrossRefGoogle Scholar
Smith, P. L. (1982). Curve fitting and modeling with splines using statistical variable selection techniques. Report 166034, NASA.
Snell, E. J. (1964). A scaling procedure for ordered categorical data. Biometrics 20, 592–607.CrossRefGoogle Scholar
Sobehart, J., S., Keenan, and R., Stein (2000). Validation methodologies for default risk models. Credit, 51–56.Google Scholar
Soofi, E. S., J. J., Retzer, and M., Yasai-Ardekani (2000). A framework for measuring the importance of variables with applications to management research and decision models. Decision Sciences 31, 595–625.CrossRefGoogle Scholar
Statnikov, A., C. F., Aliferis, I., Tsamardinos, D., Hardin, and S., Levy (2005). A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21, 631–643.CrossRefGoogle ScholarPubMed
Steadman, S. and Weissfeld, L. (1998). A study of the effect of dichotomizing ordinal data upon modelling. Communications in Statistics – Simulation and Computation 27(4), 871–887.Google Scholar
Stein, C. (1981). Estimation of the mean of a multivariate normal distribution. Annals of Statistics 9, 1135–1151.CrossRefGoogle Scholar
Steinwart, I. and Christmann, A. (2008). Support vector machines. Springer Verlag.Google Scholar
Stiratelli, R., N., Laird, and J. H., Ware (1984). Random-effects models for serial observation with binary response. Biometrics 40, 961–971.CrossRefGoogle Scholar
Stone, C., M., Hansen, C., Kooperberg, and Y., Truong (1997). Polynomial splines and their tensor products in extended linear modeling. The Annals of Statistics 25, 1371–1470.Google Scholar
Stone, C. J. (1977). Consistent nonparametric regression (with discussion). Annals of Statistics 5, 595–645.CrossRefGoogle Scholar
Stram, D. O. and Wei, L. J. (1988). Analyzing repeated measurements with possibly missing observations by modelling marginal distributions. Statistics in Medicine 7, 139–148.Google Scholar
Stram, D. O., L. J., Wei, and J. H., Ware (1988). Analysis of repeated categorical outcomes with possibly missing observations and time-dependent covariates. Journal of the American Statistical Association 83, 631–637.CrossRefGoogle Scholar
Strobl, C., A.-L., Boulesteix, and T., Augustin (2007). Unbiased split selection for classification trees based on the gini index. Computational Statistics & Data Analysis 52, 483–501.CrossRefGoogle Scholar
Strobl, C., A.-L., Boulesteix, T., Kneib, T., Augustin, and A., Zeileis (2008). Conditional variable importance for random forests. BMC Bioinformatics 9(1), 307.CrossRefGoogle ScholarPubMed
Strobl, C., J., Malley, and G., Tutz (2009). An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests. Psychological Methods 14, 323–348.CrossRefGoogle ScholarPubMed
Stroud, A. H. and Secrest, D. (1966). Gaussian Quadrature Formulas. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Stukel, T. A. (1988). Generalized logistic models. Journal of the American Statistical Association 83(402), 426–431.CrossRefGoogle Scholar
Suissa, S. and Shuster, J. J. (1991). The 2×2 method-pairs trial: Exact unconditional design and analysis. Biometrics 47, 361–372.CrossRefGoogle Scholar
Tüchler, R. (2008). Bayesian variable selection for logistic models using auxiliary mixture sampling. Journal of Computational and Graphical Statistics 17, 76–94.CrossRefGoogle Scholar
Thall, P. F. and Vail, S. C. (1990). Some covariance models for longitudinal count data with overdispersion. Biometrics 46, 657–671.CrossRefGoogle ScholarPubMed
Theil, H. (1970). On the estimation of relationships involving qualitative variables. American Journal of Sociology 76(1), 103–154.CrossRefGoogle Scholar
Thurner, P. and Eymann, A. (2000). Policy-specific alienation and indifference in the calculus of voting: A simultaneous model of party choice and abstention. Public Choice 102, 49–75.CrossRefGoogle Scholar
Thurstone, L. L. (1927). A law of comparative judgement. Psychological Review 34, 273–286.CrossRefGoogle Scholar
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society B 58, 267–288.Google Scholar
Tibshirani, R. and Hastie, T. (1987). Local likelihood estimation. Journal of the American Statistical Association 82, 559–568.CrossRefGoogle Scholar
Tibshirani, R., T., Hastie, B., Narasimhan, and G., Chu (2003). Class prediction by nearest shrunken centroids, with applications to DNA microarrays. Statistical Science, 104–117.CrossRefGoogle Scholar
Tibshirani, R., T., Hastie, B., Narasimhan, S., Soltys, G., Shi, A., Koong, and Q.-T., Le (2004). Sample classification from protein mass spectrometry, by “'peak probability contrasts”. Bioinformatics 20, 3034–3044.CrossRefGoogle Scholar
Tibshirani, R., M., Saunders, S., Rosset, J., Zhu, and K., Kneight (2005). Sparsity and smoothness via the fused lasso. Journal of the Royal Statistical Society B 67, 91–108.CrossRefGoogle Scholar
Tjur, T. (1982). A connection between Rasch's item analysis model and a multiplicative Poisson modelb. Scandinavian Journal of Statistics 9, 23–30.Google Scholar
Toledano, A. and Gatsanis, C. (1996). Ordinal regression methodology for ROC curves derived from correlated data. Statistics in Medicine 15, 1807–1826.3.0.CO;2-U>CrossRefGoogle ScholarPubMed
Troyanskaya, O. G., M. E., Garber, P. O., Brown, D., Botstein, and R. B., Altman (2002). Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics 18, 1454–1461.CrossRefGoogle ScholarPubMed
Tsiatis, A. A. (1980). A note on a goodness-of-fit test for the logistic regression model. Biometrika 67, 250–251.CrossRefGoogle Scholar
Tukey, J. (1977). Exploratory Data Analysis. Reading, Pennsylvania: Addison Wesley.Google Scholar
Tutz, G. (1986). Bradley-Terry-Luce models with an ordered response. Journal of Mathematical Psychology 30, 306–316.CrossRefGoogle Scholar
Tutz, G. (1991). Sequential models in ordinal regression. Computational Statistics & Data Analysis 11, 275–295.CrossRefGoogle Scholar
Tutz, G. (2003). Generalized semiparametrically structured ordinal models. Biometrics 59, 263–273.CrossRefGoogle ScholarPubMed
Tutz, G. (2005). Modelling of repeated ordered measurements by isotonic sequential regression. Statistical Modelling 5(4), 269–287.CrossRefGoogle Scholar
Tutz, G. and Binder, H. (2004). Flexible modelling of discrete failure time including timevarying smooth effects. Statistics in Medicine 23(15), 2445–2461.CrossRefGoogle Scholar
Tutz, G. and Binder, H. (2006). Generalized additive modeling with implicit variable selection by likelihood-based boosting. Biometrics 62, 961–971.CrossRefGoogle ScholarPubMed
Tutz, G. and Binder, H. (2007). Boosting ridge regression. Computational Statistics & Data Analysis 51, 6044–6059.CrossRefGoogle Scholar
Tutz, G. and Gertheiss, J. (2010). Feature extraction in signal regression: A boosting technique for functional data regression. Journal of Computational and Graphical Statistics 19, 154–174.CrossRefGoogle Scholar
Tutz, G. and Groll, A. (2010a). Binary and ordinal random effects models including variable selection. Technical Report 97, LMU, Department of Statistics.
Tutz, G. and Groll, A. (2010b). Generalized linear mixed models based on boosting. In Kneib, T. and Tutz, G. (Eds.), Statistical Modelling and Regression Structures – Festschrift in the Honour of Ludwig Fahrmeir, pp. 197–215. Physica.CrossRefGoogle Scholar
Tutz, G. and Hechenbichler, K. (2005). Aggregating classifiers with ordinal response structure. Journal of Statistical Computation and Simulation 75(5), 391–408.CrossRefGoogle Scholar
Tutz, G. and Hennevogl, W. (1996). Random effects in ordinal regression models. Computational Statistics and Data Analysis 22, 537–557.CrossRefGoogle Scholar
Tutz, G. and Kauermann, G. (1997). Local estimators in multivariate generalized linear models with varying coefficients. Computational Statistics 12, 193–208.Google Scholar
Tutz, G. and Leitenstorfer, F. (2006). Response shrinkage estimators in binary regression. Computational Statistics and Data Analysis 50, 2878–2901.CrossRefGoogle Scholar
Tutz, G. and Petry, S. (2011). Nonparametric estimation of the link function including variable selection. Statistics and Computing, to appear.Google Scholar
Tutz, G. and Reithinger, F. (2007). A boosting approach to flexible semiparametric mixed models. Statistics in Medicine 26, 2872–2900.CrossRefGoogle ScholarPubMed
Tutz, G. and Scholz, T. (2004). Semiparametric modelling of multicategorial data. Journal of Statistical Computation & Simulation 74, 183–200.CrossRefGoogle Scholar
Tutz, G. and Ulbricht, J. (2009). Penalized regression with correlation based penalty. Statistics and Computing 19, 239–253.CrossRefGoogle Scholar
Tversky, A. (1972). Elimination by aspects: A theory of choice. Psychological Review 79, 281–299.CrossRefGoogle Scholar
Tweedie, M. C. K. (1957). Statistical properties of inverse Gaussian distributions. I. Annals of Mathematical Statistics 28(2), 362–377.CrossRefGoogle Scholar
Ulbricht, J. and Tutz, G. (2008). Boosting correlation based penalization in generalized linear models. In , Shalabh and Heumann, C. (Eds.) Recent Advances In Linear Models and Related Areas. New York: Springer–Verlag.Google Scholar
Ulm, K. (1991). A statistical method for assessing a threshold in epidemiological studies. Statistics in Medicine 10, 341–348.CrossRefGoogle ScholarPubMed
van den Broek, J. (1995). A score test for zero inflation in a Poisson distribution. Biometrics 51(2), 738–743.CrossRefGoogle Scholar
Van der Linde, A. and Tutz, G. (2008). On association in regression: the coefficient of determination revisited. Statistics 42, 1–24.Google Scholar
van Houwelingen, J. C. and Cessie, S. L. (1990). Predictive value of statistical models. Statistics in Medicine 9, 1303–1325.CrossRefGoogle ScholarPubMed
Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer–Verlag.CrossRefGoogle Scholar
Verbeke, and Molenberghs, G. (2000). Linear Mixed Models for longitudinal data. New York: Springer–Verlag.Google Scholar
Vidakovic, (1999). Statistical Modelling by Wavelets. Wiley Series in Probability and Statistics. New York: Wiley.CrossRefGoogle Scholar
Vuong, Q. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica 2, 307–333.CrossRefGoogle Scholar
Wacholder, S. (1986). Binomial regression in GLIM: Estimation risk ratios and risk differences. American Journal of Epidemiology 123, 174–184.CrossRefGoogle Scholar
Wahba, G. (1990). Spline Models for Observational Data. Philadelphia: Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
Walker, S. H. and Duncan, D. B. (1967). Estimation of the probability of an event as a function of several independent variables. Biometrika 54, 167–178.CrossRefGoogle ScholarPubMed
Wand, M. P. (2000). A comparison of regression spline smoothing procedures. Computational Statistics 15, 443–462.CrossRefGoogle Scholar
Wand, M. P. (2003). Smoothing and mixed models. Computational Statistics 18(2), 223–249.CrossRefGoogle Scholar
Wang, H. and Xia, Y. (2009). Shrinkage estimation of the varying coefficient model. Journal of the American Statistical Association 104(486), 747–757.CrossRefGoogle Scholar
Wang, L. (2011). GEE analysis of clustered binary data with diverging number of covariates. Ann. Statist. 39, 389–417.CrossRefGoogle Scholar
Wang, Y.-F. and Carey, V. (2003). Working correlation structure missclassification, estimation and covariate design: Implicationa for generalized estimating equations. Biometrika 90, 29–41.CrossRefGoogle Scholar
Watson, G. S. (1964). Smooth regression analysis. Sankhyā, Series A, 26, 359–372.Google Scholar
Wedderburn, R. W. M. (1974). Quasilikelihood functions, generalized linear models and the Gauss-Newton method. Biometrika 61, 439–447.Google Scholar
Wei, G. and Tanner, M. (1990). A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. Journal of the American Statistical Association 85, 699–704.CrossRefGoogle Scholar
Weisberg, S. and Welsh, A. H. (1994). Adapting for the missing link. Annals of Statistics 22, 1674–1700.CrossRefGoogle Scholar
Welsh, A., X., Lin, and R., Carroll (2002). Marginal longitudinal nonparametric regression. Journal of the American Statistical Association 97(458), 482–493.CrossRefGoogle Scholar
Whittaker, J. (1990). Graphical Models in Applied Multivariate Statistics. Chichester: Wiley.Google Scholar
Whittaker, J. (2008). Graphical Models in Applied Multivariate Statistics. Wiley Publishing.Google Scholar
Whittemore, A. S. (1983). Transformations to linearity in binary regression. SIAM Journal of Applied Mathematics 43, 703–710.CrossRefGoogle Scholar
Wild, C. J. and Yee, T. W. (1996). Additive extensions to generalized estimating equation methods. Journal of the Royal Statistical Society B58, 711–725.Google Scholar
Wilkinson, G. N. and Rogers, C. E. (1973). Symbolic description of factorial models for analysis of variance. Applied Statistics 22, 392–399.CrossRefGoogle Scholar
Williams, D. A. (1982). Extra binomial variation in logistic linear models. Applied Statistics 31, 144–148.CrossRefGoogle Scholar
Williams, O. D. and Grizzle, J. E. (1972). Analysis of contingency tables having ordered response categories. Journal of the American Statistical Association 67, 55–63.CrossRefGoogle Scholar
Williamson, J. M., K., Kim, and S. R., Lipsitz (1995). Analyzing bivariate ordinal data using a global odds ratio. Journal of the American Statistical Association 90, 1432–1437.CrossRefGoogle Scholar
Winkelmann, R. (1997). Count Data Models: Econometric Theory and Application to Labor Mobility (Second Edition). Berlin: Springer-Verlag.Google Scholar
Wolfinger, R.W. (1994). Laplace's approximation for nonlinear mixed models. Biometrika 80, 791–795.Google Scholar
Wong, G. Y. and Mason, W. M. (1985). The hierarchical logistic regression model for multilevel analysis. Journal of the American Statistical Association 80, 513–524.CrossRefGoogle Scholar
Wood, S. N. (2000). Modelling and smoothing parameter estimation with multiple quadratic penalties. Journal of the Royal Statistical Society B 62, 413–428.CrossRefGoogle Scholar
Wood, S. N. (2004). Stable and efficient multiple smoothing parameter estimation for generalized additive models. Journal of the American Statistical Association 99, 673–686.CrossRefGoogle Scholar
Wood, S. N. (2006a). Generalized Additive Models: An Introduction with R. London: Chapman & Hall/CRC.Google Scholar
Wood, S. N. (2006b). On confidence intervals for generalized additive models based on penalized regression splines. Australian & New Zealand Journal of Statistics 48, 445–464.CrossRefGoogle Scholar
Wood, S. N. (2006c). Thin plate regression splines. Journal of the Royal Statistical Society, Series B 65, 95–114.Google Scholar
Wu, J. C. F. (1983). On the covergence properties of the EM-algorithm. Annals of Statistics 11, 95–103.CrossRefGoogle Scholar
Xia, T., F., Kong, S., Wang, and X., Wang (2008). Asymptotic properties of the maximum quasi-likelihood estimator in quasi-likelihood nonlinear models. Communications in Statistics – Theory and Methods 37(15), 2358–2368.CrossRefGoogle Scholar
Xia, Y., H., Tong, W. K., Li, and L., Zhu (2002). An adaptive estimation of dimension reduction. Journal of the Royal Statistical Society B 64, 363–410.CrossRefGoogle Scholar
Xie, M. and Yang, Y. (2003). Asymptotics for generalized estimating equations with large cluster sizes. The Annals of Statistics 31(1), 310–347.Google Scholar
Ye, J. M. (1998). On measuring and correcting the effects of data mining and model selection. Journal of the American Statistical Association 93(441), 120–131.CrossRefGoogle Scholar
Yee, T. (2010). The VGAM package for categorical data analysis. Journal of Statistical Software 32(10), 1–34.Google Scholar
Yee, T. and Hastie, T. (2003). Reduced-rank vector generalized linear models. Statistical Modelling 3, 15.CrossRefGoogle Scholar
Yee, T. W. and Wild, C. J. (1996). Vector generalized additive models. Journal of the Royal Statistical Society B, 481–493.Google Scholar
Yellott, J. I. (1977). The relationship between Luce's choice axiom, Thurstone's theory of comparative judgement, and the double exponential distribution. Journal of Mathematical Psychology 15, 109–144.CrossRefGoogle Scholar
Yu, Y. and Ruppert, D. (2002). Penalized spline estimation for partially linear single-index models. Journal of the American Statistical Association 97, 1042–1054.CrossRefGoogle Scholar
Yuan, M. and Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society B 68, 49–67.CrossRefGoogle Scholar
Zahid, F. M. and Tutz, G. (2009). Ridge estimation for multinomial logit models with symmetric side constraints. Technical Report 67, LMU, Department of Statistics.
Zahid, F. M. and Tutz, G. (2010). Multinomial logit models with implicit variable selection. Technical Report 89, Department of Statistics LMU.Google Scholar
Zeger, S. L. (1988). Commentary. Statistics in Medicine 7, 161–168.CrossRefGoogle Scholar
Zeger, S. L. and Diggle, P. J. (1994). Semi-parametric models for longitudinal data with application to CD4 cell numbers in HIV seroconverters. Biometrics 50, 689–699.CrossRefGoogle Scholar
Zeger, S. L. and Karim, M. R. (1991). Generalized linear models with random effects; a Gibbs' sampling approach. Journal of the American Statistical Association 86, 79–95.CrossRefGoogle Scholar
Zeileis, A., C., Kleiber, and S., Jackman (2008). Regression models for count data in R. Journal of Statistical Software 27.CrossRefGoogle Scholar
Zhang, H. (1998). Classification trees for multiple binary responses. Journal of the American Statistical Association 93, 180–193.CrossRefGoogle Scholar
Zhang, H. and Singer, B. (1999). Recursive Partitioning in the Health Sciences. New York: Springer–Verlag.CrossRefGoogle Scholar
Zhang, Q. and Ip, E. (2011). Generalized linear model for partially ordered data. Statistics in Medicine (to appear).Google ScholarPubMed
Zhao, L. P. and Prentice, R. (1990). Correlated binary regression using a quadratic exponential model. Biometrika 77, 642–48.CrossRefGoogle Scholar
Zhao, L. P., R. L., Prentice, and S., Self (1992). Multivariate mean parameter estimation by using a partly exponential model. Journal of the Royal Statistical Society B 54, 805–811.Google Scholar
Zhao, P., G., Rocha, and B., Yu (2009). The composite absolute penalties family for grouped and hierarchical variable selection. Annals of Statistics 37, 3468–3497.CrossRefGoogle Scholar
Zhao, P. and Yu, B. (2004). Boosted lasso. Technical report, University of California, Berkeley, USA.CrossRef
Zheng, B. and Agresti, A. (2000). Summarizing the predictive power of a generalized linear model. Statistics in Medicine 19, 1771–1781.3.0.CO;2-P>CrossRefGoogle ScholarPubMed
Zheng, S. (2008). Selection of components and degrees of smoothing via lasso in high dimensional nonparametric additive models. Computational Statistics & Data Analysis 53, 164–175.CrossRefGoogle Scholar
Zhu, J. and Hastie, T. (2004). Classification of gene microarrays by penalized logistic regression. Biostatistics 5, 427–443.CrossRefGoogle ScholarPubMed
Zhu, Z., W., Fung, and X., He (2008). On the asymptotics of marginal regression splines with longitudinal data. Biometrika 95(4), 907.CrossRefGoogle Scholar
Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association 101(476), 1418–1429.CrossRefGoogle Scholar
Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society B 67, 301–320.CrossRefGoogle Scholar
Zweig, M. and Campbell, G. (1993). Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clinical Chemistry 39, 561–577.Google ScholarPubMed

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  • Bibliography
  • Gerhard Tutz, Ludwig-Maximilians-Universität Munchen
  • Book: Regression for Categorical Data
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