The Annals of Statistics

Dimension reduction for censored regression data

Chun-Houh Chen, Ker-Chau Li, and Jane-Ling Wang

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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 applica-tion to a real data set are reported.

Article information

Ann. Statist., Volume 27, Number 1 (1999), 1-23.

First available in Project Euclid: 5 April 2002

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 65G05 62J20: Diagnostics

accelerated failure time model censored linear regression Cox model curse of dimensionality hazard function Kaplan-Meier estimate regression graphics sliced inverse regression survival analysis


Li, Ker-Chau; Wang, Jane-Ling; Chen, Chun-Houh. Dimension reduction for censored regression data. Ann. Statist. 27 (1999), no. 1, 1--23. doi:10.1214/aos/1018031098.

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