Local partial-likelihood estimation for lifetime data

• 成果类型: Article
• 署名作者: Fan, Jianqing; Lin, Huazhen; Zhou, Yong
• 署名单位: Chinese University of Hong Kong; Princeton University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Sichuan University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053605000000796
• 发表日期: 2006
• 页码: 290-325
• 关键词: varying-coefficient models; time-dependent coefficients; proportional hazards model; cox regression-model; variable selection; longitudinal data; Lasso

摘要

This paper considers a proportional hazards model, which allows one to examine the extent to which covariates interact nonlinearly with an exposure variable, for analysis of lifetime data. A local partial-likelihood technique is proposed to estimate nonlinear interactions. Asymptotic normality of the proposed estimator is established. The baseline hazard function, the bias and the variance of the local likelihood estimator are consistently estimated. In addition, a one-step local partial-likelihood estimator is presented to facilitate the computation of the proposed procedure and is demonstrated to be as efficient as the fully iterated local partial-likelihood estimator. Furthermore, a penalized local likelihood estimator is proposed to select important risk variables in the model. Numerical examples are used to illustrate the effectiveness of the proposed procedures.