Variable selection in L1 penalized censored regression

다국어 초록 (Multilingual Abstract)

The proposed method is based on a penalized censored regression model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log likelihood function of censored regression model. It provide the ecient computation of regression parameters including variable selection and leads to the generalized cross validation function for the model selection. Numerical results are then presented to indicate the performance of the proposed method.

참고문헌 (Reference)

1 Rosenwald, A., "The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma" 346 : 1937-1947, 2002

2 Tibshirani, R, "The lasso method for variable selection in the Cox model" 16 : 385-395, 1997

3 Hu, S, "Sparse penalization with censoring constraints for estimating high dimen-sional AFT models with applications to microarray data analysis" Case Western Reserve University 2010

4 Krishnapuram, B., "Sparse multinomial logistic regression: Fast algorithms and generalization bounds" 27 : 957-968, 2005

5 황창하, "Semiparametric support vector machine for accelerated failure time model" 한국데이터정보과학회 21 (21): 765-775, 2010

6 Ghosh, K. S, "Semiparametric accelerated failure time models for censored data" 15 : 213-229, 2006

7 Bair, E, "Semi-supervised methods to predict patient survival from gGene ex-pression data" 2 : 511-522, 2004

8 Huang, J., "Regularized estimation in the accelerated failure time model with high dimensional covariates" Department of Statistics and Actuarial Science, The University of Iowa 2005

9 Zhou, M, "Regression with censored data : The synthetic data and least squares approach" University of Kentucky 1998

10 Tibshirani, R., "Regression shrinkage and selection via the lasso" 58 : 267-288, 1996

11 Cox, D. R., "Regression models and life tables (with discussions)" 74 : 187-220, 1972

12 Koul, H., "Regression analysis with randomly right censored data" 9 : 1276-1288, 1981

13 Jin, Z., "Rank-based inference for the accelerated failure time model" 90 : 341-353, 2003

14 Kaplan, E. L, "Nonparametric estimation from incomplete observations" 53 : 457-481, 1958

15 Zhou, M, "M-estimation in censored linear models" 79 : 837-841, 1992

16 Buckley, J, "Linear regression with censored data" 66 : 429-436, 1979

17 심주용, "Kernel method for autoregressive data" 한국데이터정보과학회 20 (20): 949-954, 2009

18 Gehan, E. A., "Generalized Wilcoxon test for comparing arbitrarily singe-censored samples" 52 : 202-223, 1965

19 석경하, "Doubly penalized kernel method for heteroscedastic autoregressive data" 한국데이터정보과학회 21 (21): 155-162, 2010

20 Orbe, J., "Censored partial regression" 4 : 109-121, 2003

21 Li, H., "Censored data regression in high-dimension and low-sample size settings for genomic appli-cations" University of Pennsylvania 2006

22 심주용, "Censored Kernel Ridge Regression" 한국데이터정보과학회 16 (16): 1045-1052, 2005

23 Williams, P. M, "Bayesian regularization and pruning using a Laplace prior" 7 : 117-143, 1995

24 Akaike, H, "A new look at the statistical model identification" 19 : 716-723, 1974

25 Sauerbrei, W, "A bootstrap resampling procedure for model building: Appli-cation to the Cox regression model" 11 : 2093-2099, 1992