Quantile regression methods with varying-coefficient models for censored data

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

9 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)154-172
Journal / PublicationComputational Statistics and Data Analysis
Volume88
Online published11 Mar 2015
Publication statusPublished - Aug 2015

Abstract

Considerable intellectual progress has been made to the development of various semiparametric varying-coefficient models over the past ten to fifteen years. An important advantage of these models is that they avoid much of the curse of dimensionality problem as the nonparametric functions are restricted only to some variables. More recently, varying-coefficient methods have been applied to quantile regression modeling, but all previous studies assume that the data are fully observed. The main purpose of this paper is to develop a varying-coefficient approach to the estimation of regression quantiles under random data censoring. We use a weighted inverse probability approach to account for censoring, and propose a majorize-minimize type algorithm to optimize the non-smooth objective function. The asymptotic properties of the proposed estimator of the nonparametric functions are studied, and a resampling method is developed for obtaining the estimator of the sampling variance. An important aspect of our method is that it allows the censoring time to depend on the covariates. Additionally, we show that this varying-coefficient procedure can be further improved when implemented within a composite quantile regression framework. Composite quantile regression has recently gained considerable attention due to its ability to combine information across different quantile functions. We assess the finite sample properties of the proposed procedures in simulated studies. A real data application is also considered.

Research Area(s)

  • Composite quantile regression, Covariate-dependent censoring, Inverse probability weighting, MM algorithm, Perturbation resampling