An Adapted Loss Function for Censored Quantile Regression

TL;DR

This paper introduces a new loss function for censored quantile regression that directly incorporates censoring, enabling flexible modeling and providing a practical algorithm with theoretical guarantees and real data validation.

Contribution

The paper proposes a novel loss function for censored quantile regression that handles censoring at the loss level, facilitating various modeling approaches and establishing estimator consistency.

Findings

The proposed estimator performs well in simulations compared to existing methods.

A simple bootstrap procedure effectively supports inference.

Application to real data demonstrates the method's practical utility.

Abstract

In this paper, we study a novel approach for the estimation of quantiles when facing potential right censoring of the responses. Contrary to the existing literature on the subject, the adopted strategy of this paper is to tackle censoring at the very level of the loss function usually employed for the computation of quantiles, the so-called "check" function. For interpretation purposes, a simple comparison with the latter reveals how censoring is accounted for in the newly proposed loss function. Subsequently, when considering the inclusion of covariates for conditional quantile estimation, by defining a new general loss function, the proposed methodology opens the gate to numerous parametric, semiparametric and nonparametric modelling techniques. In order to illustrate this statement, we consider the well-studied linear regression under the usual assumption of conditional independence between the true response and the censoring variable. For practical minimization of the studied loss function, we also provide a simple algorithmic procedure shown to yield satisfactory results for the proposed estimator with respect to the existing literature in an extensive simulation study. From a more theoretical prospect, consistency of the estimator for linear regression is obtained using very recent results on non-smooth semiparametric estimation equations with an infinite-dimensional nuisance parameter, while numerical examples illustrate the adequateness of a simple bootstrap procedure for inferential purposes. Lastly, an application to a real dataset is used to further illustrate the validity and finite sample performance of the proposed estimator.