Kernel estimation in a nonparametric marker dependent hazard model

• 成果类型: Article
• 署名作者: Nielsen, JP; Linton, OB
• 署名单位: Yale University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176324321
• 发表日期: 1995
• 页码: 1735-1748
• 关键词: regression-model; counting-processes; large sample; inference; density; tests; fit

摘要

We introduce a new kernel hazard estimator in a nonparametric model where the stochastic hazard depends on the current value of time and on the current value of a time dependent covariate or marker. We establish the pointwise and global convergence of our estimator.