Objective Bayesian Inference for Repairable System Subject to Competing Risks

TL;DR

This paper develops an objective Bayesian approach for repairable systems with competing risks, providing invariant priors and closed-form estimators, and demonstrates its effectiveness on real and simulated data.

Contribution

It introduces a novel orthogonal reparametrization and objective Bayesian prior for competing risks models in repairable systems, ensuring invariance and unbiased estimators.

Findings

Posterior distribution is a product of gamma distributions with desirable properties.

Bayes estimators have closed-form expressions and are unbiased.

Method applied successfully to real datasets and validated through simulation.

Abstract

Competing risks models for a repairable system subject to several failure modes are discussed. Under minimal repair, it is assumed that each failure mode has a power law intensity. An orthogonal reparametrization is used to obtain an objective Bayesian prior which is invariant under relabelling of the failure modes. The resulting posterior is a product of gamma distributions and has appealing properties: one-to-one invariance, consistent marginalization and consistent sampling properties. Moreover, the resulting Bayes estimators have closed-form expressions and are naturally unbiased for all the parameters of the model. The methodology is applied in the analysis of (i) a previously unpublished dataset about recurrent failure history of a sugarcane harvester and (ii) records of automotive warranty claims introduced in [1]. A simulation study was carried out to study the efficiency of the methods proposed.