Variable selection for semiparametric proportional hazards model under progressive Type-II censoring. Issue 6 (3rd July 2017)

• Record Type: Journal Article
• Authors: Zhao, Xuejing; Su, Jinxia
• Is Part Of: Communications in statistics. Volume 46:Issue 6(2017)
• Page Start: 4367
• Page End: 4376
• Publication Date: 2017-07-03
• Subjects: Cox proportional hazards model -- Least absolute shrinkage and selection operator (LASSO) -- Progressive Type-II censoring scheme -- Semi-parametric estimation -- Stepwise selection method -- Variable selection; 62N01; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2015.1117637
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: Variable selection is an effective methodology for dealing with models with numerous covariates. We consider the methods of variable selection for semiparametric Cox proportional hazards model under the progressive Type-II censoring scheme. The Cox proportional hazards model is used to model the influence coefficients of the environmental covariates. By applying Breslow's "least information" idea, we obtain a profile likelihood function to estimate the coefficients. Lasso-type penalized profile likelihood estimation as well as stepwise variable selection method are explored as means to find the important covariates. Numerical simulations are conducted and Veteran's Administration Lung Cancer data are exploited to evaluate the performance of the proposed method.