Dimension reduction for censored regression data

• 成果类型: Article
• 署名作者: Li, KC; Wang, JL; Chen, CH
• 署名单位: University of California System; University of California Los Angeles; University of California System; University of California Davis; Academia Sinica - Taiwan
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 1999
• 页码: 1-23
• 关键词: sliced inverse regression; projection pursuit; models; asymptotics; link

摘要

Without parametric assumptions, high-dimensional regression analysis is already complex. This is made even harder when data are subject to censoring. In this article, we seek ways of reducing the dimensionality of the regressor before applying nonparametric smoothing techniques. If the censoring time is independent of the lifetime, then the method of sliced inverse regression can be applied directly. Otherwise, modification is needed to adjust for the censoring bias. A key identity leading to the bias correction is derived and the root-n consistency of the modified estimate is established. Patterns of censoring can also be studied under a similar dimension reduction framework. Some simulation results and an application to a real data set are reported.