A quantile regression estimator for censored data

TL;DR

This paper introduces a new censored quantile regression estimator based on unbiased estimating equations, demonstrating its consistency, asymptotic normality, and computational efficiency through simulations and real data analysis.

Contribution

It presents a novel censored quantile regression estimator with an efficient algorithm, enabling practical inference for survival data under standard assumptions.

Findings

Estimator is consistent and asymptotically normal.

Computational algorithm leverages existing quantile regression tools.

Simulation studies show good finite-sample performance.

Abstract

We propose a censored quantile regression estimator motivated by unbiased estimating equations. Under the usual conditional independence assumption of the survival time and the censoring time given the covariates, we show that the proposed estimator is consistent and asymptotically normal. We develop an efficient computational algorithm which uses existing quantile regression code. As a result, bootstrap-type inference can be efficiently implemented. We illustrate the finite-sample performance of the proposed method by simulation studies and analysis of a survival data set.