Multiple repairable systems under dependent competing risks with nonparametric Frailty

TL;DR

This paper develops a Bayesian nonparametric frailty model to analyze dependent competing risks in repairable systems, capturing heterogeneity and dependence effects for more accurate parameter estimation.

Contribution

It introduces a flexible Bayesian nonparametric approach for modeling frailty distributions in dependent competing risks, improving estimation accuracy and robustness.

Findings

Simulation studies validate the proposed method.

Real case studies demonstrate practical applicability.

Model captures dependence and heterogeneity effectively.

Abstract

The aim of this article is to analyze data from multiple repairable systems under the presence of dependent competing risks. In order to model this dependence structure, we adopted the well-known shared frailty model. This model provides a suitable theoretical basis for generating dependence between the components failure times in the dependent competing risks model. It is known that the dependence effect in this scenario influences the estimates of the model parameters. Hence, under the assumption that the cause-specific intensities follow a PLP, we propose a frailty-induced dependence approach to incorporate the dependence among the cause-specific recurrent processes. Moreover, the misspecification of the frailty distribution may lead to errors when estimating the parameters of interest. Because of this, we considered a Bayesian nonparametric approach to model the frailty density in order to offer more flexibility and to provide consistent estimates for the PLP model, as well as insights about heterogeneity among the systems. Both simulation studies and real case studies are provided to illustrate the proposed approaches and demonstrate their validity.