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Indexed by:期刊论文
Date of Publication:2011-01-01
Journal:COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
Included Journals:Scopus、SCIE
Volume:40
Issue:24
Page Number:4372-4386
ISSN No.:0361-0926
Key Words:Adaptive lasso; BIC; Oracle property; Variable selection; Weighted least squares
Abstract:This article considers the adaptive lasso procedure for the accelerated failure time model with multiple covariates based on weighted least squares method, which uses Kaplan-Meier weights to account for censoring. The adaptive lasso method can complete the variable selection and model estimation simultaneously. Under some mild conditions, the estimator is shown to have sparse and oracle properties. We use Bayesian Information Criterion (BIC) for tuning parameter selection, and a bootstrap variance approach for standard error. Simulation studies and two real data examples are carried out to investigate the performance of the proposed method.