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Activity Number: 520 - Survival Analysis III
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #329734 Presentation
Title: Transformed Dynamic Quantile Regression on Censored Data
Author(s): Tony Sit* and Gongjun Xu and Chi Wing George Chu
Companies: The Chinese University of Hong Kong and University of Michigan and Columbia University
Keywords: Censored quantile regression; Power transformation; Survival analysis

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. Moreover, the proposed approach no longer requires the potentially restrictive global linearity assumption of the martingale-based estimation approaches in the quantile regression literature on censored data. Uniform consistency and weak convergence of the proposed estimator as a process of quantile levels via the martingale-based argument are established. Numerical studies are presented to illustrate the outperformance of the proposed estimator over existing contenders under various settings.

Authors who are presenting talks have a * after their name.

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