A new non-parametric estimator of the cumulative distribution function under time-and random-censoring

TL;DR

This paper introduces a novel non-parametric estimator for the cumulative distribution function under time- and random-censoring, utilizing a likelihood approach based on reversed hazard rate, with demonstrated application to real data.

Contribution

It proposes a new non-parametric estimator for the CDF under censoring using a likelihood method based on reversed hazard rate, advancing existing estimation techniques.

Findings

New estimator performs well on real data

Provides a comprehensive review of existing estimators

Demonstrates improved estimation accuracy

Abstract

In this paper, we first provide a review of different non-parametric estimators for the cumulative distribution function under left-censoring. We then propose a new estimator based on a non-parametric likelihood approach using reversed hazard rate. Finally, we conclude with an application to a real data.