Nonparametric regression using local kernel estimating equations for correlated failure time data

• 成果类型: Article
• 署名作者: Yu, Zhangsheng; Lin, Xihong
• 署名单位: University System of Ohio; Ohio State University; Harvard University; Harvard T.H. Chan School of Public Health
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asm081
• 发表日期: 2008
• 页码: 123137
• 关键词: LIKELIHOOD; splines; MODEL

摘要

We study nonparametric regression for correlated failure time data. Kernel estimating equations are used to estimate nonparametric covariate effects. Independent and weighted-kernel estimating equations are studied. The derivative of the nonparametric function is first estimated and the nonparametric function is then estimated by integrating the derivative estimator. We show that the nonparametric kernel estimator is consistent for any arbitrary working correlation matrix and that its asymptotic variance is minimized by assuming working independence. We evaluate the performance of the proposed kernel estimator using simulation studies, and apply the proposed method to the western Kenya parasitaemia data.