Estimating multiplicative and additive hazard functions by kernel methods

• 成果类型: Article
• 署名作者: Linton, OB; Nielsen, JP; Van de Geer, S
• 署名单位: University of London; London School Economics & Political Science; Leiden University - Excl LUMC; Leiden University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 2003
• 页码: 464-492
• 关键词: nonparametric regression-models; counting-processes; efficient estimation; likelihood-estimation; cox regression; integration; inference; sample

摘要

We propose new procedures for estimating the component functions in both additive and multiplicative nonparametric marker-dependent hazard models. We work with a full counting process framework that allows for left truncation and right censoring and time-varying covariates. Our procedures are based on kernel hazard estimation as developed by Nielsen and Linton and on the idea of marginal integration. We provide a central limit theorem for the marginal integration estimator. We then define estimators based on finite-step backfitting in both additive and multiplicative cases and prove that these estimators are asymptotically normal and have smaller variance than the marginal integration method.