Dependent censoring with simultaneous death times based on the Generalized Marshall-Olkin model

TL;DR

This paper develops a model for dependent censoring with simultaneous failure times using the Generalized Marshall-Olkin model, proposing estimators and analyzing their properties with simulations and real data.

Contribution

It introduces a new application of the Marshall-Olkin model to dependent censoring with simultaneous failures, including estimators and their asymptotic properties.

Findings

Estimators are asymptotically normal under certain conditions.

Simulation studies show good finite-sample performance.

Application to real data demonstrates practical utility.

Abstract

In this paper, we considered the problem of dependent censoring models with a positive probability that the times of failure are equal. In this context, we proposed to consider the Marshall-Olkin type model and studied some properties of the associated survival copula in its application to censored data. We also introduced estimators for the marginal distributions and the joint survival probabilities under different schemes and showed their asymptotic normality under appropriate conditions. Finally, we evaluated the finite-sample performance of our approach relying on a small simulation study on synthetic data, and an application to real data.