Adaptive Lasso Variable Selection for the Accelerated Failure Models

作者：Wang, Xiaoguang^*; Song, Lixin

来源：Communications in Statistics - Theory and Methods, 2011, 40(24): 4372-4386.

DOI：10.1080/03610926.2010.513785

• Adaptive lasso
• BIC
• Oracle property
• Variable selection
• Weighted least squares

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.

• 出版日期2011
• 单位大连理工大学