Hazard Estimation under Generalized Censoring

TL;DR

This paper develops a new estimator for the cumulative hazard function under complex censoring conditions, proves its asymptotic properties, and validates bootstrap methods for inference in this setting.

Contribution

It introduces a revised hazard estimator for general censoring, proves a functional central limit theorem, and confirms bootstrap validity for practical applications.

Findings

Proposed a hazard estimator for arbitrary censoring.

Established a functional central limit theorem.

Validated bootstrap methods for the estimator.

Abstract

This paper focuses on the problem of the estimation of the cumulative hazard function of a distribution on a general complete separable metric space when the data points are subject to censoring by an arbitrary adapted random set. A problem involving observability of the estimator proposed in [8] and [9] is resolved and a functional central limit theorem is proven for the revised estimator. Several examples and applications are discussed, and the validity of bootstrap methods is established in each case.