Variable selection in semiparametric linear regression with censored data

• 成果类型: Article
• 署名作者: Johnson, Brent A.
• 署名单位: Emory University; Rollins School Public Health
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2008.00639.x
• 发表日期: 2008
• 页码: 351-370
• 关键词: prediction error property; rank-tests; lasso estimator; LARGE-SAMPLE; MODEL; parameters

摘要

We describe two procedures for selecting variables in the semiparametric linear regression model for censored data. One procedure penalizes a vector of estimating equations and simultaneously estimates regression coefficients and selects submodels. A second procedure controls systematically the proportion of unimportant variables through forward selection and the addition of pseudorandom variables. We explore both rank-based statistics and Buckley-James statistics in the setting proposed and evaluate the performance of all methods through extensive simulation studies and one real data set.