Deviance residuals based PLS regression for censored data in high dimensional setting

Chemometrics and Intelligent Laboratory Systems

Volumn 91, Issue 1, 2008, Pages 78-86

  Bastien, Philippe ^a  

^a L ORÉAL RECHERCHE   (France)

Author keywords

Cox; Deviance residuals; Kernel; LARS; Lasso; PLS; Regularization

Indexed keywords

ALGORITHM; ARTICLE; B CELL LYMPHOMA; CENSORSHIP; COMPUTER PROGRAM; GENE EXPRESSION; PARTIAL LEAST SQUARES REGRESSION; PRIORITY JOURNAL; STATISTICAL MODEL;

EID: 39649095846     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chemolab.2007.09.009     Document Type: Article

References (30)

1. Cox D.R. Regression models and life tables. Journal of the Royal Statistical Society B 74 (1972) 187-220
2. S. Wold, H. Martens, H. Wold, The multivariate calibration problem in chemistry solved by the PLS method. In Proc. Conf. Matrix Pencils, Ruhe A., Kåstrøm B. (Eds), March 1982, Lecture Notes in Mathematics, Springer Verlag, Heidelberg (1983) 286-293.
3. Tenenhaus M. La régression PLS (1998), Technip, Paris
4. Garthwaite P.H. An interpretation of partial least squares. Journal of the American Statistical Association 89 (1994) 122-127
5. Tenenhaus M. la regression logistique PLS in Proceedings of the 32èmes journées de Statistique de la Société française de Statistique. FES (1999) 721-723
6. Wold H. Estimation of principal components and related models by iterative least squares. In: Krishainaah P.R. (Ed) (1966), Multivariate Analysis. New Academic Press, New York 391-420
7. Esposito Vinzi V., and Tenenhaus M. PLS logistic regression. In: CISIA-CERESTA (Ed). PLS and Related Methods, Proceedings of the PLS'01 International Symposium (2001) 117-130
8. Bastien P., and Tenenhaus M. PLS generalised linear regression, application to the analysis of life time data. In: CISIA-CERESTA (Ed). PLS and Related Methods, Proceedings of the PLS'01 International Symposium (2001) 131-140
9. Bastien P., Esposito Vinzi V., and Tenenhaus M. PLS generalised linear regression. Computational Statistics & Data Analysis 48 (2005) 17-46
10. Bastien P. PLS-Cox model: application to gene expression data. Proceedings COMPSTAT'04 (2004), Springer: Physica-Verlag 655-662
11. Tibshirani R. The lasso method for variable selection in the Cox model. Statistics in Medicine 16 (1997) 385-395
12. Tibshirani R. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society. Series B. Methodological 58 (1996) 267-288
13. Efron B., Johnston I., Hastie T., and Tibshirani R. Least angle regression. Annals of Statistics 32 (2004) 407-451
14. Gui J., and Li H. Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with application to microarray gene expression data. Bioinformatics 21 (2005) 3001-3008
15. Segal M.R. Microarray gene expression data with linked survival phenotypes: diffuse large-B-cell lymphoma revisited. Biostatistics 7 (2006) 268-285
16. Rosenwald A., et al. The use of molecular profiling to predict survival for diffuse large-B-cell lymphoma. The New England Journal of Medicine 346 (2002) 1937-1947
17. Collett D. Modelling Survival Data in Medical Research (1994), Chapman and Hall
18. Li H., and Luan Y. Kernel Cox regression models for linking gene expression profiles to censored survival data. Pacific Symposium of Biocomputing 8 (2003) 65-76
19. Therneau T.M., and Grambasch P.M. Modeling Survival Data: Extending the Cox Model (2000), Springer, New York
20. Therneau T.M., Grambasch P.M., and Fleming T.R. Martingale-based residuals for survival models. Biometrika 77 (1990) 147-160
21. Hastie T., and Tibshirani R. Generalized Additive Models (1990), Chapman and Hall
22. J.H. Friedman, B.E. Popescu. Gradient directed regularization. Technical report, Standford University 2004.
23. De Jong S., and ter Braak C. Comments on the PLS kernel algorithm. Journal of Chemometrics 8 (1994) 169-174
24. S. Rännar, F. Lindgren, P. Geladi, S. Wold. A PLS kernel algorithm for data sets with many variables and fewer objects. Part I: Theory and Algorithm, Journal of Chemometrics, 8, 111-125.
25. Lindgren F., Geladi P., and Wold S. The kernel algorithm for PLS. Journal of Chemometrics 7 (1993) 45-59
26. Rosipal R., and Trejo L.J. Kernel partial least squares regression in reproducing kernel hilbert space. Journal of Machine Learning research 2 (2001) 97-123
27. Bennett, and Embrecht. An optimization perspective on kernel partial least squares regression, advances in learning theory: methods, models and applications. NATO Sciences Series III: Computer & Systems Sciences (2003) 227-250
28. Tenenhaus A., Giron A., Viennet E., Bera M., Saporta G., and Fertil B. Kernel logistic PLS: a tool for supervised nonlinear dimensionality reduction and binary classification. Computational Statistic & Data Analysis 51 (2007) 4083-4100
29. Heagerty P., and Zheng Y. Survival model predictive accuracy and ROC curves. UW Biostatistics Working Paper Series 219 (2003), the Berkeley Electronic Press
30. Barros A.S., and Rutledge D.N. Principal components transform-partial least squares: a novel method to accelerate cross-validation in PLS regression. Chemometrics and Intelligent Laboratory Systems 73 (2004) 245-255