Title: Rank-based inference for censored quantile regression
Authors: Tony Sit - The Chinese University of Hong Kong (Hong Kong) [presenting]
Abstract: A class of quantile regression models is proposed for time-to-event observations subject to censoring. By observing similarities between the AFT and the quantile regression models and borrowing techniques that have long been developed for the AFT model to the current setup, our framework aims at developing a more efficient for the estimating parameters of interest. Asymptotic properties including consistency and weak convergence of the proposed estimator 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.