Weighted empirical likelihood in some two-sample semiparametric models with various types of censored data

• 成果类型: Article
• 署名作者: Ren, Jian-Jian
• 署名单位: State University System of Florida; University of Central Florida
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053607000000695
• 发表日期: 2008
• 页码: 147-166
• 关键词: ratio confidence-intervals; nonparametric-estimation; asymptotic properties; self-consistent; estimators; distributions; BIAS

摘要

In this article, the weighted empirical likelihood is applied to a general setting of two-sample semiparametric models, which includes biased sampling models and case-control logistic regression models as special cases. For various types of censored data, such as right censored data, doubly censored data, interval censored data and partly interval-censored data, the weighted empirical likelihood-based semiparametric maximum likelihood estimator ((theta) over tilde (n), (F) over tilde (n)) for the underlying parameter theta(0) and distribution F(0) is derived, and the strong consistency of ((theta) over tilde (n), (F) over tilde (n)) and the asymptotic normality of (theta) over tilde (n) are established. Under biased sampling models, the weighted empirical log-likelihood ratio is shown to have an asymptotic scaled chi-squared distribution for censored data aforementioned. For right censored data, doubly censored data and partly interval-censored data, it is shown that root n((F) over tilde (n) - F(0)) weakly converges to a centered Gaussian process, which leads to a consistent goodness-of-fit test for the case-control logistic regression models.