Electronic Journal of Statistics

A quantile varying-coefficient regression approach to length-biased data modeling

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.

Article information

Source
Electron. J. Statist., Volume 8, Number 2 (2014), 2514-2540.

Dates
First available in Project Euclid: 9 December 2014

https://projecteuclid.org/euclid.ejs/1418134262

Digital Object Identifier
doi:10.1214/14-EJS959

Mathematical Reviews number (MathSciNet)
MR3285874

Zentralblatt MATH identifier
1302.62124

Citation

Chen, Xuerong; Wan, Alan T. K.; Zhou, Yong. A quantile varying-coefficient regression approach to length-biased data modeling. Electron. J. Statist. 8 (2014), no. 2, 2514--2540. doi:10.1214/14-EJS959. https://projecteuclid.org/euclid.ejs/1418134262

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