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February 1999 Dimension reduction for censored regression data
Chun-Houh Chen, Ker-Chau Li, Jane-Ling Wang
Ann. Statist. 27(1): 1-23 (February 1999). DOI: 10.1214/aos/1018031098

Abstract

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.

Citation

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Chun-Houh Chen. Ker-Chau Li. Jane-Ling Wang. "Dimension reduction for censored regression data." Ann. Statist. 27 (1) 1 - 23, February 1999. https://doi.org/10.1214/aos/1018031098

Information

Published: February 1999
First available in Project Euclid: 5 April 2002

zbMATH: 0932.62050
MathSciNet: MR1701098
Digital Object Identifier: 10.1214/aos/1018031098

Subjects:
Primary: 62J20, 65G05

Rights: Copyright © 1999 Institute of Mathematical Statistics

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Vol.27 • No. 1 • February 1999
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