Partially linear hazard regression with varying coefficients for multivariate survival data

• 成果类型: Article
• 署名作者: Cai, Jianwen; Fan, Jianqing; Jiang, Jiancheng; Zhou, Haibo
• 署名单位: University of North Carolina; University of North Carolina Charlotte; Princeton University; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2007.00630.x
• 发表日期: 2008
• 页码: 141-158
• 关键词: FAILURE TIME DATA; local partial-likelihood; estimating equations; longitudinal data; ratio parameters; models

摘要

The paper studies estimation of partially linear hazard regression models with varying coefficients for multivariate survival data. A profile pseudo-partial-likelihood estimation method is proposed. The estimation of the parameters of the linear part is accomplished via maximization of the profile pseudo-partial-likelihood, whereas the varying-coefficient functions are considered as nuisance parameters that are profiled out of the likelihood. It is shown that the estimators of the parameters are root n consistent and the estimators of the non-parametric coefficient functions achieve optimal convergence rates. Asymptotic normality is obtained for the estimators of the finite parameters and varying-coefficient functions. Consistent estimators of the asymptotic variances are derived and empirically tested, which facilitate inference for the model. We prove that the varying-coefficient functions can be estimated as well as if the parametric components were known and the failure times within each subject were independent. Simulations are conducted to demonstrate the performance of the estimators proposed. A real data set is analysed to illustrate the methodology proposed.