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2014 A quantile varying-coefficient regression approach to length-biased data modeling
Xuerong Chen, Alan T. K. Wan, Yong Zhou
Electron. J. Statist. 8(2): 2514-2540 (2014). DOI: 10.1214/14-EJS959

Abstract

Recent years have seen a growing body of literature on the analysis of length-biased data. Much of this literature adopts the accelerated failure time or proportional hazards models as the basis of study. The overwhelming majority of the existing work also assumes independence between the censoring variable and covariates. In this paper, we develop a varying-coefficient quantile regression approach to model length-biased data. Our approach does not only allow the direct estimation of the conditional quantiles of survival times based on a flexible model structure, but also has the important appeal of permitting dependence between the censoring variable and the covariates. We develop local linear estimators of the coefficients using a local inverse probability weighted estimating equation approach, and examine these estimators’ asymptotic properties. Moreover, we develop a resampling method for computing the estimators’ covariances. The small sample properties of the proposed methods are investigated in a simulation study. A real data example illustrates the application of the methods in practice.

Citation

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Xuerong Chen. Alan T. K. Wan. Yong Zhou. "A quantile varying-coefficient regression approach to length-biased data modeling." Electron. J. Statist. 8 (2) 2514 - 2540, 2014. https://doi.org/10.1214/14-EJS959

Information

Published: 2014
First available in Project Euclid: 9 December 2014

zbMATH: 1302.62124
MathSciNet: MR3285874
Digital Object Identifier: 10.1214/14-EJS959

Subjects:
Primary: 60K35, 62G08
Secondary: 62N02

Rights: Copyright © 2014 The Institute of Mathematical Statistics and the Bernoulli Society

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Vol.8 • No. 2 • 2014
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