EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 20 (2016) ESAIM: PS, 20 (2016) 463-479 References
Free Access
Issue
ESAIM: PS
Volume 20, 2016
Page(s) 463 - 479
DOI https://doi.org/10.1051/ps/2016022
Published online 30 November 2016
  1. J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981). [Google Scholar]
  2. J. Dauxois, A. Pousse and Y. Romain, Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference. J. Multivariate Anal. 12 (1982) 136–154. [CrossRef] [MathSciNet] [Google Scholar]
  3. K.A. De Jong and W.M. Spears, Using genetic algorithms to solve NP-complete problems. In Proc. of the Third International Conference on Genetic Algorithms. Edited by J.D. Schaffer (1989) 124–132. [Google Scholar]
  4. B. Efron, T. Hastie, I. Johnstone and R. Tibshirani, Least angle regression. With discussion, and a rejoinder by the authors. Ann. Stat. 32 (2004) 407–499. [CrossRef] [MathSciNet] [Google Scholar]
  5. J. Fan and R. Li, Variable selection via nonconcave penalized likelihood and its oracle properties. J. Am. Stat. Assoc. 96 (2001) 1348–1361. [CrossRef] [MathSciNet] [Google Scholar]
  6. R. Fraiman, A. Justel and M. Svarc, Selection of variables for cluster analysis and classification rules. J. Am. Stat. Assoc. 103 (2008) 1294–1303. [CrossRef] [Google Scholar]
  7. C. Fraley and A.E. Raftery, Model-based clustering, discriminant analysis, and density estimation. J. Am. Stat. Assoc. 97 (2002) 611–631. [Google Scholar]
  8. C. Fraley and A.E. Raftery, MCLUST Version 3 for R: Normal Mixture Modeling and Model-based Clustering. Technical Report No. 504, Department of Statistics, University of Washington (2009). [Google Scholar]
  9. Y. Gimenez, Selección de variables para datos multivariado y para datos funcionales. Ph.D. thesis (2015). Available at http://cms.dm.uba.ar/academico/carreras/doctorado/TesisYaninaGimenez.pdf [Google Scholar]
  10. B.E. Hansen, Uniform convergence rates for kernel estimation with dependent data. Econometric Theory 24 (2008) 726–748. [CrossRef] [MathSciNet] [Google Scholar]
  11. W.K. Härdle and L. Simar, Applied Multivariate Statistical Analysis. Springer Verlag, Berlin (2007). [Google Scholar]
  12. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning. Data mining, Inference and Prediction. Springer Verlag, Berlin (2001). [Google Scholar]
  13. X. He and P. Shi, Bivariate tensor-product B-splines in partly linear models. J. Multivariate Anal. 58 (1996) 162–181. [CrossRef] [MathSciNet] [Google Scholar]
  14. J. Hoeting, A.E. Raftrey and D. Madigan, Bayesian variable and transformation selection in linear regression. J. Comput. Graph. Statist. 11 (2002) 485–507. [CrossRef] [MathSciNet] [Google Scholar]
  15. I.T. Jolliffe, Principal Components Analysis, 2nd edition. Springer Verlag, Berlin (2002). [Google Scholar]
  16. R. Li, and G. Gong, K-NN nonparametric estimation of regression functions in the presence of irrelevant variables. Econom. J. 11 (1987) 396–408. [CrossRef] [Google Scholar]
  17. R.A. Marona, D.R. Martin and V.Y. Yohai, Robust Statistics. Theory and Methods. Wiley (2006). [Google Scholar]
  18. G.P. McCabe, Principal variables. Technometrics 26 (1984) 137–144. [CrossRef] [MathSciNet] [Google Scholar]
  19. G.A.F. Seber and A.J. Lee, Linear regression analysis, Second edition. Wiley series in probability and statistics (2005). [Google Scholar]
  20. L.J. Snell, Topics in Contemporary Probability and its Applications. CRC Press (1995). [Google Scholar]
  21. R. Tibshirani, Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58 (1996) 267–288. [Google Scholar]
  22. D.M. Witten and R. Tibshirani, Testing significance of features by lassoed principal components. Ann. Appl. Stat. 2 (2008) 986–1012. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  23. D.M. Witten and R. Tibshirani, A framework for feature selection in clustering. J. Am. Stat. Assoc. 105 (2010) 713–726. [CrossRef] [PubMed] [Google Scholar]
  24. D.M. Witten, R. Tibshirani and T. Hastie, A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10 (2009) 515–534. [CrossRef] [PubMed] [Google Scholar]
  25. C.H. Zhang, Nearly unbiased variable selection under minimax concave penalty. Ann. Stat. 38 (2010) 894–942. [CrossRef] [Google Scholar]
  26. H. Zou, T. Hastie and R. Tibshirani, Sparse principal components analysis. J. Comput. Graph. Stat. 15 (2006) 265–286. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.