Transformed Dynamic Quantile Regression on Censored Data

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

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)874-886
Journal / PublicationJournal of the American Statistical Association
Volume116
Issue number534
Online published14 Jan 2020
Publication statusPublished - 2021
Externally publishedYes

Abstract

We propose a class of power-transformed linear quantile regression models for time-to-event observations subject to censoring. By introducing a process of power transformation with different transformation parameters at individual quantile levels, our framework relaxes the assumption of logarithmic transformation on survival times and provides dynamic estimation of various quantile levels. With such formulation, our proposal no longer requires the potentially restrictive global linearity assumption imposed on a class of existing inference procedures for censored quantile regression. Uniform consistency and weak convergence of the proposed estimator as a process of quantile levels are established via the martingale-based argument. Numerical studies are presented to illustrate the outperformance of the proposed estimator over existing contenders under various settings.

Research Area(s)

  • Censored quantile regression, Empirical process, Power transformation, Survival analysis

Citation Format(s)

Transformed Dynamic Quantile Regression on Censored Data. / Chu, Chi Wing; Sit, Tony; Xu, Gongjun.
In: Journal of the American Statistical Association, Vol. 116, No. 534, 2021, p. 874-886.

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