Nonparametric likelihood ratio confidence bands for quantile functions from incomplete survival data

• 成果类型: Article
• 署名作者: Li, G; Hollander, M; McKeague, IW; Yang, J
• 署名单位: State University System of Florida; Florida State University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 1996
• 页码: 628-640
• 关键词: product-limit estimator; Empirical Likelihood; censored-data; random truncation; linear-models; probabilities; bootstrap

摘要

The purpose of this paper is to derive confidence bands for quantile functions using a nonparametric likelihood ratio approach. The method is easy to implement and has several appealing properties. It applies to right-censored and left-truncated data, and it does not involve density estimation or even require the existence of a density. Previous approaches (e.g., bootstrap) have imposed smoothness conditions on the density. The performance of the proposed method is investigated in a Monte Carlo study, and an application to real data is given.