Skip to main content Accessibility help
×
Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-20T17:51:26.293Z Has data issue: false hasContentIssue false

Bibliography

Published online by Cambridge University Press:  26 October 2018

Abdelhak M. Zoubir
Affiliation:
Technische Universität, Darmstadt, Germany
Visa Koivunen
Affiliation:
Aalto University, Finland
Esa Ollila
Affiliation:
Aalto University, Finland
Michael Muma
Affiliation:
Technische Universität, Darmstadt, Germany
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2018

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abramovich, Y. I. and Spencer, N. K. (2007). Diagonally loaded normalised sample matrix inversion (LNSMI) for outlier-resistant adaptive filtering. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., 1105–1108.CrossRefGoogle Scholar
Abramovich, Y. I. and Turcaj, P. (1999). Impulsive noise mitigation in spatial and temporal domains for surface-wave over-the-horizon radar. DTIC document. Cooperative Research Centre for Sensor Signal and Information Processing.Google Scholar
Adali, T., Schreier, P. J., and Scharf, L. L. (2011). Complex-valued signal processing: The proper way to deal with impropriety. IEEE Trans. Signal Process., 59(11): 51015125.Google Scholar
Agostinelli, C. (2004). Robust Akaike information criterion for ARMA models. Rendiconti per gli Studi Economici Quantitativi, 1: 114.Google Scholar
Aittomaki, T. and Koivunen, V. (2004). Recursive householder-based space-time processor for jammer mitigation in navigation receivers. In Proc. 38th Annu. CISS2004 Conf. Inform. Sci. Syst., 1–4.Google Scholar
Alexander, D. C., Barker, G. J., and Arridge, S. R. (2002). Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data. Magn. Reson. Med., 48(2): 331340.Google Scholar
Al-Sayed, S., Zoubir, A. M., and Sayed, A. H. (2016). Robust adaptation in impulsive noise. IEEE Trans. Signal Process., 64(11): 28512865.CrossRefGoogle Scholar
Amado, C. and Pires, A. M. (2004). Robust bootstrap with non random weights based on the influence function. Commun. Stat. Simul. Comput., 33(2): 377396.Google Scholar
Amado, C., Bianco, A. M., Boente, G., and Pires, A. M. (2014). Robust bootstrap: An alternative to bootstrapping robust estimators. REVSTAT Statist. J., 12(2): 169197.Google Scholar
Andersen, H. H., Hojbjerre, M., Sorensen, D., and Eriksen, P. S. (1995). Linear and graphical models for the multivariate complex normal distribution. Lecture Notes in Statistics, vol. 101. Springer-Verlag, New York.Google Scholar
Andrews, B. (2008). Rank-based estimation for autoregressive moving average time series models. J. Time Ser. Anal., 29(1): 5173.Google Scholar
Astola, J., Haavisto, P., and Neuvo, Y. (1990). Vector median filters. Proc. IEEE, 78(4): 678689.Google Scholar
Aysal, T. C. and Barner, K. E. (2007). Meridian filtering for robust signal processing. IEEE Trans. Signal Process., 55(8): 39493962.Google Scholar
Barner, K. E. and Arce, G. R. (2003). Nonlinear signal and image processing: Theory, methods, and applications. CRC Press, Boca Raton, FL.Google Scholar
Barrodale, I. and Roberts, F. D. K. (1973). An improved algorithm for discrete  1 linear approximation. SIAM J. Numerical Anal., 10(5): 839848.Google Scholar
Basiri, S., Ollila, E., and Koivunen, V. (2014). Fast and robust bootstrap method for testing hypotheses in the ICA model. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., 610, Florence, Italy.Google Scholar
Basiri, S., Ollila, E., and Koivunen, V. (2016). Robust, scalable, and fast bootstrap method for analyzing large scale data. IEEE Trans. Signal Process., 64(4): 10071017.Google Scholar
Basiri, S., Ollila, E., and Koivunen, V. (2017). Enhanced bootstrap method for statistical inference in the ICA model. Signal Process., 138: 5362.CrossRefGoogle Scholar
Becker, C., Fried, R., and Kuhnt, S., editors (2013). Robustness and complex data structures: Festschrift in honour of Ursula Gather. Springer, New York.Google Scholar
Belloni, F. and Koivunen, V. (2003). Unitary root-MUSIC technique for uniform circular array. In Proc. 3rd IEEE Int. Symp. Signal Process. Inform. Technol., 451454, Darmstadt, Germany.Google Scholar
Belloni, F., Richter, A., and Koivunen, V. (2007). DOA estimation via manifold separation for arbitrary array structures. IEEE Trans. Signal Process., 55(10): 48004810.Google Scholar
Bénar, C. G., Schön, D., Grimault, S., Nazarian, B., Burle, B., Roth, M., Badier, J. M., Marquis, P., Liegeois-Chauvel, C., and Anton, J. L. (2007). Single-trial analysis of oddball event-related potentials in simultaneous EEG-fMRI. Hum. Brain Map., 28(7): 602613.Google Scholar
Bernoulli, D. (1777). Dijudicatio maxime probabilis plurium observationum discrepantium atque verisimillima inductio inde formanda. Acta Acad. Sci. Petropolit., 1: 333.Google Scholar
Bickel, P. J., Götze, F., and van Zwet, W. R. (2012). Resampling fewer than n observations: Gains, losses, and remedies for losses. In Selected Works of Willem van Zwet, 267297. Springer, New York.Google Scholar
Blankenship, T. K., Kriztman, D. M., and Rappaport, T. S. (1997). Measurements and simulation of radio frequency impulsive noise in hospitals and clinics. In Proc. IEEE 47th Veh. Technol. Conf., vol. 3, 19421946, Phoenix, AZ.Google Scholar
Box, G. E. P. (1953). Non-normality and tests on variances. Biometrika, 40(3–4): 318335.CrossRefGoogle Scholar
Box, G. E. P. (1979). Robustness in the strategy of scientific model building. Robustness Stat., 1: 201236.Google Scholar
Boyd, S. and Vandenberghe, L. (2004). Convex optimization. Cambridge University Press, Cambridge.Google Scholar
Brandwood, D. H. (1983). A complex gradient operator and its applications in adaptive array theory. IEE Proc. F and H, 130(1): 1116.Google Scholar
Brillinger, D. R. (2001). Time series: Data analysis and theory. SIAM, Philadelphia, PA.CrossRefGoogle Scholar
Brodsky, B. E. and Darkhovsky, B. S. (2008). Minimax methods for multihypothesis sequential testing and change-point detection problems. Seq. Anal., 27(2): 141173.Google Scholar
Bustos, O. H. and Yohai, V. J. (1986). Robust estimates for ARMA models. J. Am. Statist. Assoc., 81(393): 155168.Google Scholar
Calvet, L. E., Czellar, V., and Ronchetti, E. (2015). Robust filtering. J. Am. Stat. Assoc., 110: 15911606.Google Scholar
Candes, E. J. and Wakin, M. B. (2008). An introduction to compressive sampling. IEEE Signal Proc. Mag., 25(2): 2130.Google Scholar
Carlson, B. D. (1988). Covariance matrix estimation errors and diagonal loading in adaptive arrays. IEEE Trans. Aerosp. Electron. Syst., 24(4): 397401.Google Scholar
Carroll, J. D. and Chang, J.-J. (1970). Analysis of individual differences in multidimensional scaling via an n-way generalization of Eckart-Young decomposition. Psychometrika, 35(3): 283319.Google Scholar
Chakhchoukh, Y. (2010a). Contribution to the estimation of SARIMA (application to short-term forecasting of electricity consumption). PhD thesis, Université de Paris-Sud, Faculté des Sciences d’Orsay, Essonne.Google Scholar
Chakhchoukh, Y. (2010b). A new robust estimation method for ARMA models. IEEE Trans. Signal Process., 58(7): 35123522.Google Scholar
Chakhchoukh, Y., Panciatici, P., and Bondon, P. (2009). Robust estimation of SARIMA models: Application to short-term load forecasting. In Proc. IEEE Workshop Statist. Signal Process7780, Cardiff, UK.Google Scholar
Chakhchoukh, Y., Panciatici, P., and Mili, L. (2010). Electric load forecasting based on statistical robust methods. IEEE Trans. Power Syst., 26(3): 982991.Google Scholar
Chen, Y., So, H. C., and Sun, W. (2014). ℓp -norm based iterative adaptive approach for robust spectral analysis. Signal Process., 94(1): 144148.Google Scholar
Chen, Y., Wiesel, A., and Hero, A. O. (2011). Robust shrinkage estimation of high-dimensional covariance matrices. IEEE Trans. Signal Process., 59(9): 40974107.Google Scholar
Chen, Y., Wiesel, A., Eldar, Y. C., and Hero, A. O. (2010). Shrinkage algorithms for MMSE covariance estimation. IEEE Trans. Signal Process., 58(10): 50165029.Google Scholar
Cheung, K. W., So, H.-C., Ma, W.-K., and Chan, Y.-T. (2004). Least squares algorithms for time-of-arrival-based mobile location. IEEE Trans. Signal Process., 52(4): 11211130.CrossRefGoogle Scholar
Chi, E. and Kolda, T. G. (2011). Making tensor factorizations robust to non-Gaussian noise. Technical Report Tech. Rep. No. SAND2011–1877, Sandia National Laboratories, Livermore, CA.Google Scholar
Cichocki, A., Mandic, D., De Lathauwer, L., Zhou, G., Zhao, Q., Caiafa, C., and Phan, A.-H. (2015). Tensor decompositions for signal processing applications: From two-way to multiway component analysis. IEEE Signal Process. Mag., 32(2): 145163.Google Scholar
Conte, E., De Maio, A., and Ricci, G. (2002). Recursive estimation of the covariance matrix of a compound-Gaussian process and its application to adaptive CFAR detection. IEEE Trans. Signal Process., 50(8): 19081915.Google Scholar
Costa, M. and Koivunen, V. (2014). Application of manifold separation to polarimetric capon beamformer and source tracking. IEEE Trans. Signal Process., 62(4): 813827.Google Scholar
Costa, M., Koivunen, V., and Viberg, M. (2013). Array processing in the face of nonidealities, signal processing reference (handbook of signal processing), vol. 3, ch. 19. Academic Press Library in Signal Processing.Google Scholar
Costa, M., Richter, A., and Koivunen, V. (2010). Unified array manifold decomposition based on spherical harmonics and 2-D Fourier basis. IEEE Trans. Signal Process., 58(9): 46344645.Google Scholar
Couillet, R. and McKay, M. (2014). Large dimensional analysis and optimization of robust shrinkage covariance matrix estimators. J. Multivariate Anal., 131: 99120.Google Scholar
Croux, C. (1998). Limit behavior of the empirical influence function of the median. Stat. Prob. Lett., 37(4): 331340.Google Scholar
Croux, C. and Exterkate, P. (2011). Robust and sparse factor modelling. Technical Report Tech. Rep. No. KBI 1120, KU Leuven, Faculty of Business and Economics, Flanders, Belgium.Google Scholar
Croux, C., Filzmoser, P., and Oliveira, M. R. (2007). Algorithms for projection–pursuit robust principal component analysis. Chemom. Intell. Lab. Syst., 87(2): 218225.Google Scholar
Dabak, A. G. and Johnson, D. H. (1993). Geometrically based robust detection. In Proc. Inform. Science Syst. Conf., 73–77.Google Scholar
Davies, P. L. (1987). Asymptotic behaviour of S-estimates of multivariate location parameters and dispersion matrices. Ann. Stat., 15(3): 12691292.Google Scholar
Davies, P. L. (1993). Aspects of robust linear regression. Ann. Stat., 21(4): 18431899.CrossRefGoogle Scholar
DeGroot, M. H. (1960). Minimax sequential tests of some composite hypotheses. Ann. Math. Stat., 31(4): 11931200.Google Scholar
Dehling, H., Fried, R., and Wendler, M. (2015). A robust method for shift detection in time series. arXiv preprint arXiv:1506.03345.Google Scholar
de Luna, X. and Genton, M. G. (2001). Robust simulation-based estimation of ARMA models. J. Comput. Graph. Stat., 10(2): 370387.Google Scholar
Demitri, N. and Zoubir, A. M. (2013). Mean-shift based algorithm for the measurement of blood glucose in hand-held devices. In Proc. 21st Eur. Signal Process. Conf., 15, Marrakech, Morocco.Google Scholar
Demitri, N. and Zoubir, A. M. (2014). A robust kernel density estimator based mean-shift algorithm. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., 79647968, Florence, Italy.Google Scholar
Demitri, N. and Zoubir, A. M. (2017). Measuring blood glucose concentrations in photometric glucometers requiring very small sample volumes. IEEE Trans. Biomed. Eng., 64(1): 2839.Google Scholar
De Moivre, A. (1733). Approximatio ad summam terminorum binomii (a+b) n in seriem expansi. Suppl. Miscellanea Analytica.Google Scholar
Deutsch, S. J., Richards, J. E., and Swain, J. J. (1990). Effects of a single outlier on ARMA identification. Commun. Stat. Theory, 19(6): 22072227.Google Scholar
Djurovic, I., Stankovic, L., and Böhme, J. F. (2003). Robust l-estimation based forms of signal transforms and time-frequency representations. IEEE Trans. Signal Process., 51(7): 1753– 1761.Google Scholar
Dong, H., Wang, Z., and Gao, H. (2010). Robust H∞ filtering for a class of nonlinear networked systems with multiple stochastic communication delays and packet dropouts. IEEE Trans. Signal Process., 58(4): 19571966.Google Scholar
Donoho, D. L. (2006). Compressive sensing. IEEE Trans. Inf. Theory, 52(2): 54065425.Google Scholar
Donoho, D. L. and Huber, P. J. (1983). The notion of breakdown point. A festschrift for Erich L. Lehmann, 157184, Wadsworth, Belmont, CA.Google Scholar
Dryden, I. L. and Mardia, K. V. (1998). Statistical shape analysis. Wiley, Chichester, UK.Google Scholar
Duncan, D. B. and Horn, S. D. (1972). Linear dynamic recursive estimation from the viewpoint of regression analysis. J. Am. Statist. Assoc., 67(340): 815821.Google Scholar
Durovic, Z. M. and Kovacevic, B. D. (1999). Robust estimation with unknown noise statistics. IEEE Trans. Autom. Control, 44(6): 12921296.Google Scholar
Dürre, A., Fried, R., and Liboschik, T. (2015). Robust estimation of (partial) autocorrelation. Wiley Interdisc. Rev. Comput. Statist., 7(3): 205222.Google Scholar
Efron, B. (1979). Bootstap methods: Another look at the jackknife. Ann. Stat., 7(1): 126.Google Scholar
Efron, B. and Tibshirani, R. J. (1994). An introduction to the bootstrap. CRC Press, Boca Raton, FL.Google Scholar
Eriksson, J., Ollila, E., and Koivunen, V. (2009). Statistics for complex random variables revisited. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., 35653568, Taipei, Taiwan.Google Scholar
Eriksson, J., Ollila, E., and Koivunen, V. (2010). Essential statistics and tools for complex random variables. IEEE Trans. Signal Process., 58(10): 54005408.Google Scholar
Etter, P. C. (2003). Underwater acoustic modeling and simulation. Taylor & Francis, Boca Raton, FL.Google Scholar
Fang, K.-T., Kotz, S., and Ng, K. W. (1990). Symmetric multivariate and related distributions. Chapman and Hall, London.Google Scholar
Fante, R. L. and Vaccaro, J. J. (2000). Wideband cancellation of interference in a GPS receive array. IEEE Trans. Aerosp. Electron. Syst., 36(2): 549564.Google Scholar
Fauß, M. (2016). Design and analysis of optimal and minimax robust sequential hypothesis tests. PhD thesis, Technische Universität Darmstadt, Darmstadt, Germany.Google Scholar
Fauß, M. and Zoubir, A. M. (2015). A linear programming approach to sequential hypothesis testing. Seq. Anal., 34(2): 235263.Google Scholar
Fauß, M. and Zoubir, A. M. (2016). Old bands, new tracks – Revisiting the band model for robust hypothesis testing. IEEE Trans. Signal Process., 64(22): 58755886.Google Scholar
Fellouris, G. and Tartakovsky, A. G. (2012). Nearly minimax one-sided mixture-based sequential tests. Seq. Anal., 31(3): 297325.Google Scholar
Fisher, R. A. (1925). Theory of statistical estimation. Math. Proc. Cambridge Philosoph. Soc., 22(5): 700725.Google Scholar
Frahm, G. (2004). Generalized elliptical distributions: Theory and applications. PhD thesis, Universität zu Köln.Google Scholar
Frank, L. E. and Friedman, J. H. (1993). A statistical view of some chemometrics regression tools. Technometrics, 35(2): 109135.Google Scholar
Friedman, J., Hastie, T., Höfling, H., and Tibshirani, R. (2007). Pathwise coordinate optimization. Ann. Appl. Stat., 1(2): 302332.Google Scholar
Fu, W. J. (1998). Penalized regressions: The bridge versus the Lasso. J. Comput. Graph. Stat., 7(3): 397416.Google Scholar
Galilei, G. (1632). Dialogue concerning the two chief world systems, Folio Society, London.Google Scholar
Gandhi, M. A. and Mili, L. (2010). Robust Kalman filter based on a generalized maximum-likelihood-type estimator. IEEE Trans. Signal Process., 58(5): 25092520.Google Scholar
Gao, X. and Huang, J. (2010). Asymptotic analysis of high-dimensional LAD regression with Lasso. Stat. Sin., 1: 14851506.Google Scholar
Gauss, C. F. (1809). Theoria Motus Corporum Celestium. Perthes et Besser.Google Scholar
Genton, M. G. and Lucas, A. (2003). Comprehensive definitions of breakdown points for independent and dependent observations. J. R. Statist. Soc. B, 65(1): 8194.Google Scholar
Gershman, A. B. (2004). Robustness issues in adaptive beamforming and high-resolution direction finding, ch. 2. In Hua et al., High-resolution and robust signal processing (2004).Google Scholar
Gerstoft, P., Xenaki, A., and Mecklenbräuker, C. F. (2015). Multiple and single snapshot compressive beamforming. J. Acoust. Soc. Am., 138(4): 20032014.Google Scholar
Gini, C. (1921). Sull’interpolazione di una retta quando i valori della variabile indipendente sono affetti da errori accidentali. Metron, 1: 6382.Google Scholar
Gini, F. (1997). Sub-optimum coherent radar detection in a mixture of K-distributed and Gaussian clutter. IEE Proc. Radar, Sonar Navig., 144(1): 3948.Google Scholar
Gini, F. and Greco, M. (2002). Covariance matrix estimation for CFAR detection in correlated heavy tailed clutter. Signal Process., 82(12): 18471859.Google Scholar
Godara, L. C. (2004). Smart antennas. CRC Press, Boca Raton, FL.Google Scholar
Goldstein, J. S., Reed, I. S., and Scharf, L. L. (1998). A multistage representation of the Wiener filter based on orthogonal projections. IEEE Trans. Inf. Theory, 44(7): 29432959.Google Scholar
Gonzalez, J. G. and Arce, G. R. (2001). Optimality of the myriad filter in practical impulsive-noise environments. IEEE Trans. Signal Process., 49(2): 438441.Google Scholar
Goodman, N. R. (1963). Statistical analysis based on certain multivariate complex Gaussian distribution (an introduction). Ann. Math. Stat., 34(1): 152177.Google Scholar
Guerci, B., Drouin, P., Grangé, V., Bougnères, P., Fontaine, P., Kerlan, V., Passa, P., Thivolet, C., Vialettes, B., and Charbonnel, B. (2003). Self-monitoring of blood glucose significantly improves metabolic control in patients with type 2 diabetes mellitus: The Auto-Surveillance Intervention Active (ASIA) study. Diabetes Metab., 29(6): 587594.Google Scholar
Gül, G. (2017). Robust and distributed hypothesis testing. Lecture Notes in Electrical Engineering. Springer International Publishing, New York.Google Scholar
Gül, G. and Zoubir, A. M. (2016). Robust hypothesis testing with α-divergence. IEEE Trans. Signal Process., 64(18): 47374750.Google Scholar
Gül, G. and Zoubir, A. M. (2017). Minimax robust hypothesis testing. IEEE Trans. Inf. Theory, 63(9): 55725587.Google Scholar
Guvenc, I. and Chong, C.-C. (2009). A survey on TOA based wireless localization and NLOS mitigation techniques. IEEE Commun. Surveys Tuts., 11(3): 107124.Google Scholar
Hammes, U. and Zoubir, A. M. (2011). Robust MT tracking based on M-estimation and interacting multiple model algorithm. IEEE Trans. Signal Process., 59(7): 33983409.Google Scholar
Hammes, U., Wolsztynski, E., and Zoubir, A. M. (2008). Transformation-based robust semipara-metric estimation. IEEE Signal Process. Lett., 15: 845848.Google Scholar
Hammes, U., Wolsztynski, E., and Zoubir, A. M. (2009). Robust tracking and geolocation for wireless networks in NLOS environments. IEEE J. Sel. Topics Signal Process., 3(5): 889901.Google Scholar
Hampel, F. R. (1968). Contributions to the theory of robust estimation. PhD thesis, University of California, Berkeley.Google Scholar
Hampel, F. R. (1974). The influence curve and its role in robust estimation. J. Am. Statist. Assoc., 69(346): 383393.Google Scholar
Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., and Stahel, W. A. (2011). Robust statistics: The approach based on influence functions, vol. 114. John Wiley & Sons, Toronto, ON.Google Scholar
Han, B., Muma, M., Feng, M., and Zoubir, A. M. (2013). An online approach for intracranial pressure forecasting based on signal decomposition and robust statistics. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., 62396243, Vancouver, BC.Google Scholar
Hannan, E. J. and Quinn, B. G. (1979). The determination of the order of an autoregression. J. R. Statist. Soc. B, 41(2): 190195.Google Scholar
Harshman, R. A. (1970). Foundations of the PARAFAC procedure: Models and conditions for an explanatory multi-modal factor analysis. UCLA working papers phonetics, 16: 184.Google Scholar
Hastie, T., Tibshirani, R., and Friedman, J. (2001). The elements of statistical learning. Springer, New York.Google Scholar
Hastie, T., Tibshirani, R., and Wainwright, M. (2015). Statistical learning with sparsity: The Lasso and generalizations. CRC Press, Boca Raton, FL.Google Scholar
Hjorungnes, A. and Gesbert, D. (2007). Complex-valued matrix differentiation: Techniques and key results. IEEE Trans. Signal Process., 55: 27402746.Google Scholar
Hoctor, R. T. and Kassam, S. A. (1990). The unifying role of the coarray in aperture synthesis for coherent and incoherent imaging. Proc. IEEE, 78(4): 735752.Google Scholar
Hoerl, A. E. and Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1): 5567.Google Scholar
Horn, R. A. and Johnson, C. A. (1985). Matrix analysis. Cambridge University Press, Cambridge.Google Scholar
Hua, Y., Gershman, A., and Cheng, Q., editors (2004). High-resolution and robust signal processing. Marcel Dekker, New York.Google Scholar
Huber, P. J. (1964). Robust estimation of a location parameter. Ann. Math. Stat., 35(1): 73101.Google Scholar
Huber, P. J. (1965). A robust version of the probability ratio test. Ann. Math. Stat., 36(6): 1753– 1758.Google Scholar
Huber, P. J. (1972). The 1972 Wald lecture robust statistics: A review. Ann. Math. Stat., 43(4): 10411067.Google Scholar
Huber, P. J. and Ronchetti, E. M. (2009). Robust statistics. John Wiley & Sons, Hoboken, NJ.Google Scholar
Huber, P. J. and Strassen, V. (1973). Minimax tests and the Neyman–Pearson lemma for capacities. Ann. Statist., 1(2): 251263.Google Scholar
Hubert, M., Van Kerckhoven, J., and Verdonck, T. (2012). Robust PARAFAC for incomplete data. J. Chemometrics, 26(6): 290298.Google Scholar
Hunter, D. R. and Lange, K. (2004). A tutorial on MM algorithms. Am. Stat., 58(1): 3037.Google Scholar
Jaeckel, L. A. (1972). Estimating regression coefficients by minimizing the dispersion of the residuals. Ann. Math. Stat., 43(5): 14491458.Google Scholar
Jansson, M., Swindlehurst, A. L., and Ottersten, B. (1998). Weighted subspace fitting for general array error models. IEEE Trans. Signal Process., 46(9): 24842498.Google Scholar
Jones, R. H. (1980). Maximum likelihood fitting of ARMA models to time series with missing observations. Technometrics, 22(3): 389395.Google Scholar
Kalluri, S. and Arce, G. R. (2000). Fast algorithms for weighted myriad computation by fixed-point search. IEEE Trans. Signal Process., 48(1): 159171.Google Scholar
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng., 82: 3545.Google Scholar
Kassam, S. A. (1981). Robust hypothesis testing for bounded classes of probability densities (Corresp.). IEEE Trans. Inf. Theory.Google Scholar
Kassam, S. A. and Poor, H. V. (1985). Robust techniques for signal processing: A survey. Proc. IEEE, 73(3): 433481.Google Scholar
Kassam, S. A. and Thomas, J. B. (1975). A class of nonparametric detectors for dependent input data. IEEE Trans. Inf. Theory, 21(4): 431437.Google Scholar
Katkovnik, V. (1998). Robust M-periodogram. IEEE Trans. Signal Process., 46(11): 31043109.Google Scholar
Kelava, A., Muma, M., Deja, M., Dagdagan, J. Y., and Zoubir, A. M. (2014). A new approach for the quantification of synchrony of multivariate non-stationary psychophysiological variables during emotion eliciting stimuli. Front. Psychol., 5.Google Scholar
Kent, J. T. (1997). Data analysis for shapes and images. J. Stat. Plan. Inference, 57(2): 181193.Google Scholar
Kent, J. T. and Tyler, D. E. (1988). Maximum likelihood estimation for the wrapped Cauchy distribution. J. Appl. Stat., 15(2): 247254.Google Scholar
Kent, J. T. and Tyler, D. E. (1991). Redescending M-estimates of multivariate location and scatter. Ann. Stat., 19(4): 21022119.Google Scholar
Kim, H.-J., Ollila, E., and Koivunen, V. (2013a). Sparse regularization of tensor decompositions. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., 38363840, Vancouver, BC.Google Scholar
Kim, H.-J., Ollila, E., and Koivunen, V. (2015). New robust Lasso method based on ranks. In Proc. 23rd Eur. Signal Process. Conf., 699703, Nice, France.Google Scholar
Kim, H.-J., Ollila, E., Koivunen, V., and Croux, C. (2013b). Robust and sparse estimation of tensor decompositions. In Proc. IEEE Global Conf. Signal Inform. Process., 965968, Austin, TX.Google Scholar
Kim, H.-J., Ollila, E., Koivunen, V., and Poor, H. V. (2014). Robust iteratively reweighted Lasso for sparse tensor factorizations. In Proc. IEEE Workshop Statist. Signal Process., 420423, Gold Coast, Australia.Google Scholar
Kim, K. and Shevlyakov, G. (2008). Why Gaussianity? IEEE Signal Process. Mag., 25(2): 102– 113.Google Scholar
Kleiner, A., Talwalkar, A., Sarkar, P., and Jordan, M. I. (2014). A scalable bootstrap for massive data. J. R. Statist. Soc. B, 76(4): 795816.Google Scholar
Koenker, R. (2005). Quantile regression. Cambridge University Press, New York.Google Scholar
Koivunen, V. (1996). Nonlinear filtering of multivariate images under robust error criterion. IEEE Trans. Image Process., 5(6): 10541060.Google Scholar
Koivunen, V. and Ollila, E. (2014). Model order selection, signal processing reference (handbook of signal processing), ch. 2. Vol. 3 of Zoubir et al., Array and statistical processing (2014).Google Scholar
Kolda, T. G. and Bader, B. W. (2009). Tensor decompositions and applications. SIAM Rev., 51(3): 455500.Google Scholar
Kozick, R. J. and Sadler, B. M. (2000). Maximum-likelihood array processing in non-Gaussian noise with Gaussian mixtures. IEEE Trans. Signal Process., 48(12): 35203535.Google Scholar
Kraut, S., Scharf, L. L., and Butler, R. W. (2005). The adaptive coherence estimator: A uniformly most-powerful-invariant adaptive detection statistic. IEEE Trans. Signal Process., 53(2): 427– 438.Google Scholar
Kraut, S., Scharf, L. L., and McWhorter, L. T. (2001). Adaptive subspace detectors. IEEE Trans. Signal Process., 49(1): 116.Google Scholar
Kreutz-Delgado, K. (2007). The complex gradient operator and the CR-calculus. Lecture notes supplement [online].Google Scholar
Krim, H. and Viberg, M. (1996). Two decades of array signal processing research: The parametric approach. IEEE Signal Process. Mag., 13(4): 6794.Google Scholar
Krishnaiah, P. R. and Lin, J. (1986). Complex elliptically symmetric distributions. Commun. Stat. Theory Methods, 15(12): 36933718.Google Scholar
Kumar, T. A. and Rao, K. D. (2009). A new M-estimator based robust multiuser detection in flat-fading non-Gaussian channels. IEEE Trans. Commun., 57(7): 19081913.Google Scholar
Künsch, H. (1984). Infinitesimal robustness for autoregressive processes. Ann. Stat., 12(3): 843– 863.Google Scholar
Le, N. D., Raftery, A. E., and Martin, R. D. (1996). Robust Bayesian model selection for autoregressive processes with additive outliers. J. Am. Stat. Assoc., 91(433): 123131.Google Scholar
Ledoit, O. and Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. J. Multivariate Anal., 88(2): 365411.Google Scholar
Lee, Y. and Kassam, S. A. (1985). Generalized median filtering and related nonlinear filtering techniques. IEEE Trans. Acoust. Speech Signal Process., 33(3): 672683.Google Scholar
Lehmann, E. L. and D’Abrera, H. J. (1975). Nonparametrics: Statistical methods based on ranks. Holden-Day, Oxford.Google Scholar
Levy, B. C. (2009). Robust hypothesis testing with a relative entropy tolerance. IEEE Trans. Inf. Theory, 55(1): 413421.Google Scholar
Li, J., Stoica, P., and Wang, Z. (2003). On robust Capon beamforming and diagonal loading. IEEE Trans. Signal Process., 51(7): 1702 – 1715.Google Scholar
Li, T.-H. (2008). Laplace periodogram for time series analysis. J. Am. Stat. Assoc., 103(482): 757768.Google Scholar
Li, T.-H. (2010). A nonlinear method for robust spectral analysis. IEEE Trans. Signal Process., 58(5): 24662474.Google Scholar
Li, Y. and Arce, G. R. (2004). A maximum likelihood approach to least absolute deviation regression. EURASIP J. Adv. Signal Process., 2004(12): 17621769.Google Scholar
Ljung, G. M. (1993). On outlier detection in time series. J. R. Statist. Soc. B, 55(2): 559567.Google Scholar
Lopuhaa, H. P. and Rousseeuw, P. J. (1991). Breakdown points of affine equivariant estimators of multivariate location and covariance matrices. Ann. Stat., 19(1): 229248.Google Scholar
Louni, H. (2008). Outlier detection in ARMA models. J. Time Ser. Anal., 29(6): 10571065.Google Scholar
Madsen, K., Nielsen, H. B., and Tingleff, O. (2004). Methods for non-linear least squares problems. Lecture note.Google Scholar
Mandic, D. P. and Goh, V. S. L. (2009). Complex valued nonlinear adaptive filters: Noncircularity, widely linear and neural models, vol. 59. John Wiley & Sons, Hoboken, NJ.Google Scholar
Markets and Markets. (2016). Location-based services (LBS) and real time location systems (RTLS) market by location (indoor and outdoor), technology (context aware, UWB, BT/BLE, beacons, A-GPS), software, hardware, service and application area – Global forecast to 2021. www.marketsandmarkets.com.Google Scholar
Maronna, R. A. (1976). Robust M-estimators of multivariate location and scatter. Ann. Stat., 5(1): 5167.Google Scholar
Maronna, R. A. and Yohai, V. J. (2008). Robust low-rank approximation of data matrices with elementwise contamination. Technometrics, 50(3): 295304.Google Scholar
Maronna, R. A., Martin, R. D., and Yohai, V. J. (2006). Robust statistics theory and methods. John Wiley & Sons, Ltd, Chichester, UK.Google Scholar
Marple, S. L. (1987). Digital spectral analysis with applications, vol. 5. Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
Martin, R. D. and Thomson, D. J. (1982). Robust-resistant spectrum estimation. Proc. IEEE, 70(9): 10971115.Google Scholar
Martin, R. D. and Yohai, V. J. (1984). Influence function for time series. Technical Report 51, Department of Statistics, University of Washington, Seattle, WA.Google Scholar
Martin, R. D. and Yohai, V. J. (1986). Influence functionals for time series. Ann. Stat., 14(3): 781818.Google Scholar
Martin, R. D., Samarov, A., and Vandaele, W. (1982). Robust methods for ARIMA models. Technical Report 21, Department of Statistics, University of Washington, Seattle, WA.Google Scholar
Martinez-Camara, M., Béjar Haro, B., Stohl, A., and Vetterli, M. (2014). A robust method for inverse transport modelling of atmospheric emissions using blind outlier detection. Geosci. Model Dev., 7: 23032311.Google Scholar
Masreliez, C. (1975). Approximate non-Gaussian filtering with linear state and observation relations. IEEE Trans. Autom. Control, 20(1): 107110.Google Scholar
Mayo, M. S. and Gray, J. B. (1997). Elemental subsets: The building blocks of regression. Am. Stat., 51(2): 122129.Google Scholar
McQuarrie, A. D. and Tsai, C.-L. (2003). Outlier detections in autoregressive models. J. Comput. Graph. Stat., 12(2): 450471.Google Scholar
Middleton, D. (1996). An introduction to statistical communication theory: An IEEE Press classic reissue, vol. 1. Wiley-IEEE Press, Hoboken, NJ.Google Scholar
Middleton, D. (1999). Non-Gaussian noise models in signal processing for telecommunications: New methods and results for class A and class B noise models. IEEE Trans. Inf. Theory, 45(4): 11291149.Google Scholar
Mili, L., Cheniae, M. G., and Rousseeuw, P. J. (2002). Robust state estimation of electric power systems. IEEE Trans. Circuits Syst. I, 41(5): 349358.Google Scholar
Moses, R. L., Simonypté, V., Stoica, P., and Söderström, T. (1994). An efficient linear method for ARMA spectral estimation. Int. J. Control, 59(2): 337356.Google Scholar
Muler, N., Peña, D., and Yohai, V. J. (2009). Robust estimation for ARMA models. Ann. Stat., 37(2): 816840.Google Scholar
Müller, S. and Welsh, A. H. (2005). Outlier robust model selection in linear regression. J. Am. Stat. Assoc., 100(472): 12971310.Google Scholar
Muma, M. (2014). Robust model order selection for ARMA models based on the bounded innovation propagation τ-estimator. In Proc. IEEE Workshop Statist. Signal Process., 428431, Gold Coast, Australia.Google Scholar
Muma, M. and Zoubir, A. M. (2017). Bounded influence propagation τ-estimation: A new method for ARMA model estimation. IEEE Trans. Signal Process., 65: 17121727.Google Scholar
Nassar, M., Gulati, K., Sujeeth, A. K., Aghasadeghi, N., Evans, B. L., and Tinsley, K. R. (2008). Mitigating near-field interference in laptop embedded wireless transceivers. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., 14051408, Las Vegas, NV.Google Scholar
Oja, H. (2013). Multivariate median, ch. 1. In Becker et al., Robustness and complex data structures (2013).Google Scholar
Ollila, E. (2010). Contributions to independent component analysis, sensor array and complex-valued signal processing. PhD thesis, Aalto University, Espoo, Finland.Google Scholar
Ollila, E. (2015a). Modern nonparametric, robust and multivariate methods: A festschrift in honor of Professor Hannu Oja. Springer, Basel, Switzerland.Google Scholar
Ollila, E. (2015b). Multichannel sparse recovery of complex-valued signals using Huber’s criterion. In Proc. Compressed Sens. Theory Appl. Radar, Sonar Remote Sens., 3236, Pisa, Italy.Google Scholar
Ollila, E. (2016a). Adaptive Lasso based on joint M-estimation of regression and scale. In Proc. 24th Eur. Signal Process. Conf., 21912195, Budapest, Hungary.Google Scholar
Ollila, E. (2016b). Direction of arrival estimation using robust complex Lasso. In Proc. 10th Eur. Conf. Antennas Propag., 15, Davos, Switzerland.Google Scholar
Ollila, E. (2017). Optimal high-dimensional shrinkage covariance estimation for elliptical distributions. In Proc. 25th Eur. Signal Process. Conf., 16891693, Kos, Greece.Google Scholar
Ollila, E. and Koivunen, V. (2003a). Influence functions for array covariance matrix estimators. In Proc. IEEE Workshop Statist. Signal Process., 462465, St. Louis, MS.Google Scholar
Ollila, E. and Koivunen, V. (2003b). Robust antenna array processing using M-estimators of pseudo-covariance. In 14th IEEE Proc. Pers. Indoor Mobile Radio Commun., vol. 3, 2659– 2663, Beijing, China.Google Scholar
Ollila, E. and Koivunen, V. (2004). Generalized complex elliptical distributions. In Proc. IEEE Sensor Array Multichannel Signal Process. Workshop, 460464, Barcelona, Spain.Google Scholar
Ollila, E. and Tyler, D. E. (2012). Distribution-free detection under complex elliptically symmetric clutter distribution. In Proc. IEEE 7th Sensor Array Multichannel Signal Process. Workshop, 413416, Wiley, Hoboken, NJ.Google Scholar
Ollila, E. and Tyler, D. E. (2014). Regularized M-estimators of scatter matrix. IEEE Trans. Signal Process., 62(22): 60596070.Google Scholar
Ollila, E., Croux, C., and Oja, H. (2004). Influence function and asymptotic efficiency of the affine equivariant rank covariance matrix. Stat. Sin., 14(1): 297316.Google Scholar
Ollila, E., Eriksson, J., and Koivunen, V. (2011a). Complex elliptically symmetric random variables – generation, characterization, and circularity tests. IEEE Trans. Signal Process., 59(1): 5869.Google Scholar
Ollila, E., Koivunen, V., and Poor, H. V. (2011b). Complex-valued signal processing – Essential models, tools and statistics. In Inf. Theory Appl. Workshop, 110. IEEE.Google Scholar
Ollila, E., Oja, H., and Croux, C. (2003a). The affine equivariant sign covariance matrix: Asymptotic behavior and efficiencies. J. Multivariate Anal., 87(2): 328355.Google Scholar
Ollila, E., Oja, H., and Koivunen, V. (2003b). Estimates of regression coefficients based on lift rank covariance matrix. J. Am. Stat. Assoc., 98(461): 9098.Google Scholar
Ollila, E., Quattropani, L., and Koivunen, V. (2003c). Robust space-time scatter matrix estimator for broadband antenna arrays. In Proc. IEEE 58th Veh. Technol. Conf., vol. 1, 5559, Orlando, FL.Google Scholar
Ollila, E., Soloveychik, I., Tyler, D. E., and Wiesel, A. (2016). Simultaneous penalized M-estimation of covariance matrices using geodesically convex optimization. Arxiv:1608.08126v1.Google Scholar
Ollila, E., Tyler, D. E., Koivunen, V., and Poor, H. V. (2012). Complex elliptically symmetric distributions: Survey, new results and applications. IEEE Trans. Signal Process., 60(11): 5597– 5625.Google Scholar
Österreicher, F. (1978). On the construction of least favourable pairs of distributions. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete, 43(1): 4955.Google Scholar
Owen, A. B. (2007). A robust hybrid of Lasso and ridge regression. Contemp. Math., 443: 5972.Google Scholar
Pan, J. and Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng., 32(3): 230236.Google Scholar
Pang, Y., Li, X., and Yuan, Y. (2010). Robust tensor analysis with L1-norm. IEEE Trans. Circuits Syst. Video Technol., 20(2): 172178.Google Scholar
Papalexakis, E. E. and Sidiropoulos, N. D. (2011). Co-clustering as multilinear decomposition with sparse latent factors. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., 20642067, Prague, Czech Republic.Google Scholar
Pascal, F., Chitour, Y., and Quek, Y. (2014). Generalized robust shrinkage estimator and its application to STAP detection problem. IEEE Trans. Signal Process., 62(21): 56405651.Google Scholar
Pascal, F., Chitour, Y., Ovarlez, J.-P., Forster, P., and Larzabal, P. (2008). Covariance structure maximum-likelihood estimates in compound Gaussian noise: Existence and algorithm analysis. IEEE Trans. Signal Process., 56(1): 3448.Google Scholar
Patwari, N., Hero, A. O., Perkins, M., Correal, N. S., and O’dea, R. J. (2003). Relative location estimation in wireless sensor networks. IEEE Trans. Signal Process., 51(8): 21372148.Google Scholar
Pedersen, K. I., Mogensen, P. E., and Fleury, B. H. (2000). A stochastic model of the temporal and azimuthal dispersion seen at the base station in outdoor propagation environments. IEEE Trans. Veh. Technol., 49(2): 437447.Google Scholar
Pesavento, M., Gershman, A. B., and Wong, K. M. (2002). Direction finding in partly calibrated sensor arrays composed of multiple subarrays. IEEE Trans. Signal Process., 50(9): 21032115.Google Scholar
Picinbono, B. (1994). On circularity. IEEE Trans. Signal Process., 42(12): 34733482.Google Scholar
Pillai, S. U. and Kwon, B. H. (1989). Forward/backward spatial smoothing techniques for coherent signal identification. IEEE Trans. Acoust. Speech Signal Process., 37(1): 815.Google Scholar
Pitas, I. and Venetsanopoulos, A. N. (2013). Nonlinear digital filters: Principles and applications, vol. 84. Springer Science & Business Media, Berlin, Germany.Google Scholar
Poincaré, H. (1904). L’état actuel et l’avenir de la physique mathématique.Google Scholar
Poor, H. V. and Thomas, J. B. (1993). Advances in statistical signal processing, signal detection (a research series), vol. 2. Jai Press, Greenwich, CT.Google Scholar
Priestley, M. B. (1981). Spectral analysis and time series. Academic Press, New York.Google Scholar
Prieto, J. C., Croux, C., and Jiménez, A. R. (2009). RoPEUS: A new robust algorithm for static positioning in ultrasonic systems. Sensors, 9(6): 42114229.Google Scholar
Prokhorov, Y. V. (1956). Convergence of random processes and limit theorems in probability theory. Theory Probab. Appl., 1(2): 157214.Google Scholar
Rao, B. D. and Hari, K. V. S. (1988). Performance analysis of subspace based methods (plane wave direction of arrival estimation). In Proc. 4th Annu. ASSP Workshop Spectr. Estimation Modeling, 9297, Minneapolis, MN.Google Scholar
Razaviyayn, M., Hong, M., and Luo, Z. (2013). A unified convergence analysis of block successive minimization methods for nonsmooth optimization. SIAM J. Optim., 23(2): 1126– 1153.Google Scholar
Reeds, J. A. (1985). Asymptotic number of roots of Cauchy location likelihood equations. Ann. Stat., 13(2): 775784.Google Scholar
Remmert, R. (1991). Theory of complex functions. Springer-Verlag, New York.Google Scholar
Ribeiro, C. B., Ollila, E., and Koivunen, V. (2005). Propagation parameter estimation in MIMO systems using mixture of angular distributions model. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., vol. 4, 885888, Philadelphia, PA.Google Scholar
Ronchetti, E. (1997). Robustness aspects of model choice. Stat. Sin., 7: 327338.Google Scholar
Rousseeuw, P. J. (1984). Least median of squares regression. J. Am. Statist. Assoc., 79(388): 871– 880.Google Scholar
Rousseeuw, P. J. (1985). Multivariate estimation with high breakdown point. Mathematical statistics and applications. Reidel, Dordrecht, the Netherlands.Google Scholar
Rousseeuw, P. J. and Leroy, A. M. (2005). Robust regression and outlier detection, vol. 589. John Wiley & Sons, Hoboken, NJ.Google Scholar
Rousseeuw, P. and Yohai, V. J. (1984). Robust regression by means of S-estimators. In Robust and nonlinear time series analysis, 256272. Springer, Berlin, Germany.Google Scholar
Rousseeuw, P. J. and Van Driessen, K. (2006). Computing LTS regression for large data sets. Data Min. Knowl. Discov., 12(1): 2945.Google Scholar
Ruckdeschel, P., Spangl, B., and Pupashenko, D. (2014). Robust Kalman tracking and smoothing with propagating and non-propagating outliers. Statist. Papers, 55(1): 93123.Google Scholar
Rudin, L. I., Osher, S., and Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena, 60: 259268.Google Scholar
Salibián-Barrera, M. and Yohai, V. J. (2012). A fast algorithm for S-regression estimates. J. Comput. Graph. Stat.Google Scholar
Salibián-Barrera, M. and Zamar, R. H. (2002). Bootstrapping robust estimates of regression. Ann. Stat., 30(2): 556582.Google Scholar
Salibián-Barrera, M., Van Aelst, S., and Willems, G. (2008a). Fast and robust bootstrap. Statist. Methods Appl., 17(1): 4171.Google Scholar
Salibián-Barrera, M., Willems, G., and Zamar, R. (2008b). The fast-τ estimator for regression. J. Comput. Graph. Stat., 17(3): 659682.Google Scholar
Salmi, J., Richter, A., and Koivunen, V. (2009). Sequential unfolding SVD for tensors with applications in array signal processing. IEEE Trans. Signal Process., 57(12): 47194733.Google Scholar
Schäck, T., Harb, Y. S., Muma, M., and Zoubir, A. M. (2017). Computationally efficient algorithm for photoplethysmography-based atrial fibrillation detection using smartphones. In Proc. 39th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 104108, Seogwipo, South Korea.Google Scholar
Schäck, T., Sledz, C., Muma, M., and Zoubir, A. M. (2015). A new method for heart rate monitoring during physical exercise using photoplethysmographic signals. In Proc. 23rd Eur. Signal Process. Conf., 26662670, Nice, France.Google Scholar
Scharf, L. L. and Friedlander, B. (1994). Matched subspace detectors. IEEE Trans. Signal Process., 42(8): 21462157.Google Scholar
Scharf, L. L. and McWhorter, L. T. (1996). Adaptive matched subspace detectors and adaptive coherence estimators. In Proc. 30th Asilomar Conf. Signals Syst. Comput., 11141117, Pacific Grove, CA.Google Scholar
Schick, I. C. and Mitter, S. K. (1994). Robust recursive estimation in the presence of heavy-tailed observation noise. Ann. Stat., 22(2): 10451080.Google Scholar
Schreier, P. J. and Scharf, L. L. (2010). Statistical signal processing of complex-valued data: The theory of improper and noncircular signals. Cambridge University Press, New York.Google Scholar
Schwarz, G. (1978). Estimating the dimension of a model. Ann. Stat., 6(2): 461464.Google Scholar
Shariati, N., Shahriari, H., and Shafaei, R. (2014). Parameter estimation of autoregressive models using the iteratively robust filtered fast-τ method. Commun. Stat. Theory Methods, 43(21): 44454470.Google Scholar
Sharif, W., Muma, M., and Zoubir, A. M. (2013). Robustness analysis of spatial time-frequency distributions based on the influence function. IEEE Trans. Signal Process., 61(8): 19581971.Google Scholar
Shi, P. and Tsai, C. L. (1998). A note on the unification of the Akaike information criterion. J. R. Statist. Soc. B, 60(3): 551558.Google Scholar
Sidiropoulos, N. D., De Lathauwer, L., Fu, X., Huang, K., Papalexakis, E. E., and Faloutsos, C. (2017). Tensor decomposition for signal processing and machine learning. IEEE Trans. Signal Process., 65(13): 35513582.Google Scholar
Siegel, A. F. (1982). Robust regression using repeated medians. Biometrika, 69(1): 242244.Google Scholar
Singh, K. (1998). Breakdown theory for bootstrap quantiles. Ann. Stat., 26(5): 17191732.Google Scholar
Sion, M. (1958). On general minimax theorems. Pac. J. Math., 8: 171176.Google Scholar
Soloveychik, I. and Wiesel, A. (2015). Performance analysis of Tyler’s covariance estimator. IEEE Trans. Signal Process., 63(2): 418426.Google Scholar
Song, I., Bae, J., and Kim, S. Y. (2002). Advanced theory of signal detection: Weak signal detection in generalized observations. Springer, Berlin, Germany.Google Scholar
Spangl, B. and Dutter, R. (2007). Estimating spectral density functions robustly. REVSTAT Statist. J., 5(1): 4161.Google Scholar
Stahl, S. (2006). The evolution of the normal distribution. Math. Mag., 79(2): 96113.Google Scholar
Stewart, C. V. (1999). Robust parameter estimation in computer vision. SIAM Rev., 41(3): 513– 537.Google Scholar
Stigler, S. M. (1973). Simon Newcomb, Percy Daniell, and the history of robust estimation, 1885– 1920. J. Am. Statist. Assoc., 68(344): 872879.Google Scholar
Stockinger, N. and Dutter, R. (1987). Robust time series analysis: A survey. Kybernetika, 23(1): 388.Google Scholar
Stoica, P. and Selen, Y. (2004). Model-order selection: A review of information criterion rules. IEEE Signal Process. Mag., 21(4): 3647.Google Scholar
Stranger, B. E., Nica, A. C., Forrest, M. S., Dimas, A., Bird, C. P., Beazley, C., Ingle, C. E., Dunning, M., Flicek, P., and Koller, D. (2007). Population genomics of human gene expression. Nat. Genet., 39(10): 12171224.Google Scholar
Strasser, F., Muma, M., and Zoubir, A. M. (2012). Motion artifact removal in ECG signals using multi-resolution thresholding. In Proc. Eur. Signal Process. Conf., 899903, Bucharest, Romania.Google Scholar
Stromberg, A. J. (1997). Robust covariance estimates based on resampling. J. Stat. Plan. Inference, 57(2): 321334.Google Scholar
Sun, Y., Babu, P., and Palomar, D. P. (2014). Regularized Tyler’s scatter estimator: Existence, uniqueness, and algorithms. IEEE Trans. Signal Process., 62(19): 51435156.Google Scholar
Sun, Y., Babu, P., and Palomar, D. P. (2017). Majorization-minimization algorithms in signal processing, communications, and machine learning. IEEE Trans. Signal Process., 65(3): 794– 816.Google Scholar
Swami, A. and Sadler, B. M. (2002). On some detection and estimation problems in heavy-tailed noise. Signal Process., 82(12): 18291846.Google Scholar
Swindlehurst, A. L. and Kailath, T. (1992). A performance analysis of subspace-based methods in the presence of model errors, part I: The MUSIC algorithm. IEEE Trans. Signal Process., 40(7): 17581774.Google Scholar
Swindlehurst, A. L. and Kailath, T. (1993). A performance analysis of subspace-based methods in the presence of model errors: Part II – Multidimensional algorithms. IEEE Trans. Signal Process., 41(9): 28822890.Google Scholar
Tabassum, M. N. and Ollila, E. (2016). Single-snapshot DOA estimation using adaptive elastic net in the complex domain. In Proc. 4th Int. Workshop Compressed Sens. Theory Appl. Radar Sonar Remote Sens., 197201, Aachen, Germany.Google Scholar
Tamada, J. A., Lesho, M., and Tierney, M. J. (2002). Keeping watch on glucose. IEEE Spectr., 39(4): 5257.Google Scholar
Tatsuoka, K. S. and Tyler, D. E. (2000). On the uniqueness of S-functionals and M-functionals under nonelliptical distributions. Ann. Stat., 28(4): 12191243.Google Scholar
Tatum, L. G. and Hurvich, C. M. (1993). High breakdown methods of time series analysis. J. R. Statist. Soc. B, 55(4): 881896.Google Scholar
Theil, H. (1950). A rank-invariant method of linear and polynomial regression analysis. Proc. R. Neth. Acad. Sci., 53: 13971412.Google Scholar
Thomas, L. and Mili, L. (2007). A robust GM-estimator for the automated detection of external defects on barked hardwood logs and stems. IEEE Trans. Signal Process., 55(7): 35683576.Google Scholar
Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. J. R. Statist. Soc. B, 58: 267288.Google Scholar
Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., and Knight, K. (2005). Sparsity and smoothness via the fused Lasso. J. R. Statist. Soc. B, 67(1): 91108.Google Scholar
Tsay, R. S. (1988). Outliers, level shifts, and variance changes in time series. J. Forecasting, 7(1): 120.Google Scholar
Tseng, P. (2001). Convergence of a block coordinate descent method for nondifferentiable minimization. J. Optim. Theory Appl., 109(3): 475494.Google Scholar
Tukey, J. W. (1960). A survey of sampling from contaminated distributions. Contrib Prob. Stat., 2: 448485.Google Scholar
Tukey, J. W. (1977). Exploratory data analysis, vol. 1. Addison-Wesley Publishing Company, Boston, MA.Google Scholar
Tyler, D. E. (1987). A distribution-free M-estimator of multivariate scatter. Ann. Stat., 15(1): 234– 251.Google Scholar
van den Bos, A. (1994). Complex gradient and Hessian. IEE Proc. Vis. Image Signal Process., 141(6): 380382.Google Scholar
van den Bos, A. (1995). The multivariate complex normal distribution – A generalization. IEEE Trans. Inf. Theory, 41(2): 537539.Google Scholar
Vardi, Y. and Zhang, C. H. (2000). The multivariate L1-median and associated data depth. Proc. Nat/. Acad. Sci., 97(4): 14231426.Google Scholar
Vastola, K. and Poor, H. V. (1984). Robust Wiener-Kolmogorov theory. IEEE Trans. Inf. Theory, 30(2): 316327.Google Scholar
Visuri, S., Koivunen, V., and Oja, H. (2000a). Sign and rank covariance matrices. J. Stat. Plan. Inference, 91: 557575.Google Scholar
Visuri, S., Oja, H., and Koivunen, V. (2000b). Nonparametric method for subspace based frequency estimation. In Proc. 10th Eur. Signal Process. Conf., 12611264, Tampere, Finland.Google Scholar
Visuri, S., Oja, H., and Koivunen, V. (2001). Subspace-based direction-of-arrival estimation using nonparametric statistics. IEEE Trans. Signal Process., 49(9): 20602073.Google Scholar
Vlaski, S. and Zoubir, A. M. (2014). Robust bootstrap based observation classification for Kalman filtering in harsh LOS/NLOS environments. In Proc. IEEE Workshop Statist. Signal Process., 332335, Gold Coast, Australia.Google Scholar
Vlaski, S., Muma, M., and Zoubir, A. M. (2014). Robust bootstrap methods with an application to geolocation in harsh LOS/NLOS environments. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., 79887992, Florence, Italy.Google Scholar
Vorobyov, S., Gershman, A., and Luo, Z.-Q. (2003). Robust adaptive beamforming using worst-case performance optimization: A solution to the signal mismatch problem. IEEE Trans. Signal Process., 51(2): 313324.Google Scholar
Vorobyov, S. A., Rong, Y., Sidiropoulos, N. D., and Gershman, A. B. (2005). Robust iterative fitting of multilinear models. IEEE Trans. Signal Process., 53(8): 26782689.Google Scholar
Wang, H., Li, G., and Jiang, G. (2007). Robust regression shrinkage and consistent variable selection through the LAD-Lasso. J. Bus. Econ. Stat., 25(3): 347355.Google Scholar
Wang, X. and Poor, H. V. (1999). Robust multiuser detection in non-Gaussian channels. IEEE Trans. Signal Process., 47(2): 289305.Google Scholar
Weiss, A. J. and Friedlander, B. (1989). Array shape calibration using sources in unknown locations – A maximum likelihood approach. IEEE Trans. Acoust. Speech Signal Process., 37(12): 19581966.Google Scholar
Weiszfeld, E. (1937). Sur le point pour lequel la somme des distances de n points donnés est minimum. Tohoku Math. J., 43: 355386.Google Scholar
Werner, S., With, M., and Koivunen, V. (2007). Householder multistage Wiener filter for space-time navigation receivers. IEEE Trans Aerosp. Electron. Syst., 43(3).Google Scholar
Wiesel, A. (2012). Geodesic convexity and covariance estimation. IEEE Trans. Signal Process., 60(12): 61826189.Google Scholar
Wiesel, A. and Zhang, T. (2015). Structured robust covariance estimation. Found. Trends Signal Process., 8(3): 127216.Google Scholar
Williams, D. B. and Johnson, D. H. (1993). Robust estimation of structured covariance matrices. IEEE Trans. Signal Process., 41(9): 28912906.Google Scholar
Wu, T. T. and Lange, K. (2008). Coordinate descent algorithms for Lasso penalized regression. Ann. Appl. Stat., 2(1): 224244.Google Scholar
Wu, T. T., Chen, Y. F., Hastie, T., Sobel, E., and Lange, K. (2009). Genome-wide association analysis by Lasso penalized logistic regression. Bioinformatics, 25(6): 714721.Google Scholar
Ye, M., Haralick, R. M., and Shapiro, L. G. (2003). Estimating piecewise-smooth optical flow with global matching and graduated optimization. IEEE Trans. Pattern Anal. Mach. Intell., 25(12): 16251630.Google Scholar
Yohai, V. J. (1987). High breakdown-point and high efficiency robust estimates for regression. Ann. Stat., 15(2): 642656.Google Scholar
Yohai, V. J. and Zamar, R. H. (1988). High breakdown-point estimates of regression by means of the minimization of an efficient scale. J. Am. Statist. Assoc., 83(402): 406413.Google Scholar
Zhang, T. and Wiesel, A. (2016). Automatic diagonal loading for Tyler’s robust covariance estimator. In Proc. IEEE Workshop Statist. Signal Process., 1–5.Google Scholar
Zhang, T., Wiesel, A., and Greco, M. S. (2013). Multivariate generalized Gaussian distribution: Convexity and graphical models. IEEE Trans. Signal Process., 61(16): 41414148.Google Scholar
Zoltowski, M. D., Kautz, G. M., and Silverstein, S. D. (1993). Beamspace root-MUSIC. IEEE Trans. Signal Process., 41(1): 344364.Google Scholar
Zou, H. (2006). The adaptive Lasso and its oracle properties. J. Am. Stat. Assoc., 101: 14181429.Google Scholar
Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. J. R. Statist. Soc. B, 67(2): 301320.Google Scholar
Zoubir, A. M. (2014). Introduction to Statistical Signal Processing, ch. 1. Vol. 3 of Zoubir et al., Array and statistical signal processing (2014).Google Scholar
Zoubir, A. M. and Boashash, B. (1998). The bootstrap and its application in signal processing. IEEE Signal Process. Mag., 15(1): 5676.Google Scholar
Zoubir, A. M. and Brcich, R. F. (2002). Multiuser detection in heavy tailed noise. Digit. Signal Process., 12(2–3): 262273.Google Scholar
Zoubir, A. M. and Iskander, D. R. (2004). Bootstrap techniques for signal processing. Cambridge University Press, Cambridge.Google Scholar
Zoubir, A. M. and Iskander, D. R. (2007). Bootstrap methods and applications. IEEE Signal Process. Mag., 24(4): 1019.Google Scholar
Zoubir, A. M., Koivunen, V., Chakhchoukh, Y., and Muma, M. (2012). Robust estimation in signal processing: A tutorial-style treatment of fundamental concepts. IEEE Signal Process. Mag., 29(4): 6180.Google Scholar
Zoubir, A. M., Viberg, M., Chellappa, R., and Theodoridis, S., editors (2014). Array and statistical signal processing, vol. 3. Academic Press Library in Signal Processing.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • Bibliography
  • Abdelhak M. Zoubir, Technische Universität, Darmstadt, Germany, Visa Koivunen, Aalto University, Finland, Esa Ollila, Aalto University, Finland, Michael Muma, Technische Universität, Darmstadt, Germany
  • Book: Robust Statistics for Signal Processing
  • Online publication: 26 October 2018
  • Chapter DOI: https://doi.org/10.1017/9781139084291.013
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Bibliography
  • Abdelhak M. Zoubir, Technische Universität, Darmstadt, Germany, Visa Koivunen, Aalto University, Finland, Esa Ollila, Aalto University, Finland, Michael Muma, Technische Universität, Darmstadt, Germany
  • Book: Robust Statistics for Signal Processing
  • Online publication: 26 October 2018
  • Chapter DOI: https://doi.org/10.1017/9781139084291.013
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Bibliography
  • Abdelhak M. Zoubir, Technische Universität, Darmstadt, Germany, Visa Koivunen, Aalto University, Finland, Esa Ollila, Aalto University, Finland, Michael Muma, Technische Universität, Darmstadt, Germany
  • Book: Robust Statistics for Signal Processing
  • Online publication: 26 October 2018
  • Chapter DOI: https://doi.org/10.1017/9781139084291.013
Available formats
×