On a semiparametric survival model with flexible covariate effect

• 成果类型: Article
• 署名作者: Nielsen, JP; Linton, O; Bickel, PJ
• 署名单位: Yale University; University of California System; University of California Berkeley
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 1998
• 页码: 215-241
• 关键词: kaplan-meier estimate; counting-processes; uniform consistency; linear-regression; kernel; derivatives; INFORMATION; hazards; FAMILY

摘要

A semiparametric hazard model with parametrized time but general covariate dependency is formulated and analyzed inside the framework of counting process theory. A profile likelihood principle is introduced for estimation of the parameters: the resulting estimator is n(1/2)-consistent, asymptotically normal and achieves the semiparametric efficiency bound. An estimation procedure for the nonparametric part is also given and its asymptotic properties are derived. We provide an application to mortality data.