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论文类型：期刊论文

发表时间：2011-01-01

发表刊物：COMMUNICATIONS IN STATISTICS-THEORY AND METHODS

收录刊物：Scopus、SCIE

卷号：40

期号：24

页面范围：4372-4386

ISSN号：0361-0926

关键字：Adaptive lasso; BIC; Oracle property; Variable selection; Weighted least squares

摘要：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.