Transformed Dynamic Quantile Regression on Censored Data. Issue 534 (3rd April 2021)

• Record Type: Journal Article
• Authors: Chu, Chi Wing; Sit, Tony; Xu, Gongjun
• Is Part Of: Journal of the American Statistical Association. Volume 116:Issue 534(2021)
• Page Start: 874
• Page End: 886
• Publication Date: 2021-04-03
• Subjects: Censored quantile regression -- Power transformation -- Empirical process -- Survival analysis; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2019.1695623
• Languages: English
• ISSNs: 0162-1459

Abstract

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.