Electronic Journal of Statistics

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

Xuerong Chen, Alan T. K. Wan, and Yong Zhou

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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

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

First available in Project Euclid: 9 December 2014

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Zentralblatt MATH identifier

Primary: 62G08: Nonparametric regression 60K35: Interacting random processes; statistical mechanics type models; percolation theory [See also 82B43, 82C43]
Secondary: 62N02: Estimation

Estimating equation length-biased local linear prevalent cohort quantile regression resampling method right-censored varying-coefficient


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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