Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Ronchetti, Elvezio
and
Staudte, Robert G.
1994.
A Robust Version of Mallows's C P.
Journal of the American Statistical Association,
Vol. 89,
Issue. 426,
p.
550.
Koenker, Roger
and
Schorfheide, Frank
1994.
Quantile spline models for global temperature change.
Climatic Change,
Vol. 28,
Issue. 4,
p.
395.
Glendinning, R. H.
1996.
Identifying infinite variance arma models using a robust pukk1la koreisha kallinen strategy.
Communications in Statistics - Theory and Methods,
Vol. 25,
Issue. 12,
p.
3027.
Qian, Guoqi
and
Künsch, Hans R.
1998.
On model selection via stochastic complexity in robust linear regression.
Journal of Statistical Planning and Inference,
Vol. 75,
Issue. 1,
p.
91.
Qian, Guoqi
1999.
Computations and analysis in robust regression model selection using stochastic complexity.
Computational Statistics,
Vol. 14,
Issue. 3,
p.
293.
Morgenthaler, Stephan
and
Schumacher, Martin M.
1999.
Robust analysis of a response surface design.
Chemometrics and Intelligent Laboratory Systems,
Vol. 47,
Issue. 1,
p.
127.
Bubna, Kishore
and
Stewart, Charles V.
2000.
Model Selection Techniques and Merging Rules for Range Data Segmentation Algorithms.
Computer Vision and Image Understanding,
Vol. 80,
Issue. 2,
p.
215.
WU, Y.
2001.
An m-estimation-based model selection criterion with a data-oriented penalty.
Journal of Statistical Computation and Simulation,
Vol. 70,
Issue. 1,
p.
71.
Rivers, Douglas
and
Vuong, Quang
2002.
Model selection tests for nonlinear dynamic models.
The Econometrics Journal,
Vol. 5,
Issue. 1,
p.
1.
Inglot, Tadeusz
and
Kallenberg, Wilbert C. M.
2003.
Moderate deviations of minimum contrast estimators under contamination.
The Annals of Statistics,
Vol. 31,
Issue. 3,
Mahadevan, V.
Narasimha-Iyer, H.
Roysam, B.
and
Tanenbaum, H.L.
2004.
Robust Model-Based Vasculature Detection in Noisy Biomedical Images.
IEEE Transactions on Information Technology in Biomedicine,
Vol. 8,
Issue. 3,
p.
360.
Jiang, Wenxin
and
Liu, Xiangyang
2004.
Consistent model selection based on parameter estimates.
Journal of Statistical Planning and Inference,
Vol. 121,
Issue. 2,
p.
265.
Cade, Brian S.
Noon, Barry R.
and
Flather, Curtis H.
2005.
QUANTILE REGRESSION REVEALS HIDDEN BIAS AND UNCERTAINTY IN HABITAT MODELS.
Ecology,
Vol. 86,
Issue. 3,
p.
786.
Bednarski, Tadeusz
and
Mocarska, Edyta
2006.
On robust model selection within the Cox model.
The Econometrics Journal,
Vol. 9,
Issue. 2,
p.
279.
Baierl, Andreas
Futschik, Andreas
Bogdan, Małgorzata
and
Biecek, Przemysław
2007.
Locating multiple interacting quantitative trait loci using robust model selection.
Computational Statistics & Data Analysis,
Vol. 51,
Issue. 12,
p.
6423.
Rao, C. R.
Wu, Y.
and
Shao, Q.
2007.
An M-Estimation-Based Procedure for Determining the Number of Regression Models in Regression Clustering.
Journal of Applied Mathematics and Decision Sciences,
Vol. 2007,
Issue. ,
p.
1.
Narasimha-Iyer, Harihar
Beach, James M.
Khoobehi, Bahram
and
Roysam, Badrinath
2007.
Automatic Identification of Retinal Arteries and Veins From Dual-Wavelength Images Using Structural and Functional Features.
IEEE Transactions on Biomedical Engineering,
Vol. 54,
Issue. 8,
p.
1427.
Preminger, Arie
and
Sakata, Shinichi
2007.
A model selection method for S‐estimation.
The Econometrics Journal,
Vol. 10,
Issue. 2,
p.
294.
Cai, Zongwu
and
Xu, Xiaoping
2009.
Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models.
Journal of the American Statistical Association,
Vol. 104,
Issue. 485,
p.
371.
Almpanidis, G.
Kotti, M.
and
Kotropoulos, C.
2009.
Robust Detection of Phone Boundaries Using Model Selection Criteria With Few Observations.
IEEE Transactions on Audio, Speech, and Language Processing,
Vol. 17,
Issue. 2,
p.
287.