Efficient estimation of the partly linear additive Cox model

• 成果类型: Article
• 署名作者: Huang, J
• 署名单位: University of Iowa
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1017939141
• 发表日期: 1999
• 页码: 1536-1563
• 关键词: PROPORTIONAL HAZARDS MODEL; regression-models; large sample; CONVERGENCE; INFORMATION

摘要

The partly linear additive Cox model is an extension of the (linear) Cox model and allows flexible modeling of covariate effects; semiparametrically. We study asymptotic properties of the maximum partial likelihood estimator of this model with right-censored data using; polynomial splines. We show that, with a range of choices of the smoothing parameter (the number of spline basis functions) required for estimation of the nonparametric components, the estimator of the finite-dimensional regression parameter is root-n consistent, asymptotically normal and achieves the semiparametric information bound. Rates of convergence for the estimators of the nonparametric components are obtained. They are comparable to the rates in nonparametric regression. implementation of the estimation approach can be done easily and is illustrated by using a simulated example.