Vairiable selection for multivariate failure time data

• 成果类型: Article
• 署名作者: Cai, JW; Fan, JQ; Li, RZ; Zhou, HB
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; Princeton University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/92.2.303
• 发表日期: 2005
• 页码: 303316
• 关键词: PROPORTIONAL HAZARDS MODEL; variable selection; regression-models; ratio parameters; likelihood; number; Lasso

摘要

In this paper, we propose a penalised pseudo-partial likelihood method for variable selection with multivariate failure time data with a growing number of regression coefficients. Under certain regularity conditions, we show the consistency and asymptotic normality of the penalised likelihood estimators. We further demonstrate that, for certain penalty functions with proper choices of regularisation parameters, the resulting estimator can correctly identify the true model, as if it were known in advance. Based on a simple approximation of the penalty function, the proposed method can be easily carried out with the Newton-Raphson algorithm. We conduct extensive Monte Carlo simulation studies to assess the finite sample performance of the proposed procedures. We illustrate the proposed method by analysing a dataset from the Framingham Heart Study.