Bayesian prediction of minimal repair times of a series system based on hybrid censored sample of components' lifetimes under Rayleigh distribution

TL;DR

This paper develops Bayesian methods to predict minimal repair times for a series system with Rayleigh-distributed component lifetimes, using hybrid censored data, and demonstrates their effectiveness through real data and simulations.

Contribution

It introduces Bayesian predictive procedures for minimal repair times based on hybrid censored samples under Rayleigh distribution, which is a novel approach.

Findings

Proposed Bayesian predictors perform well in simulations.

Real data example validates the predictive methods.

Comparison shows advantages over existing approaches.

Abstract

In this paper, we develop Bayesian predictive inferential procedures for prediction of repair times of a series system, applying a minimal repair strategy, using the information contained in an independent observed hybrid censored sample of the lifetimes of the components of the system, assuming the underlying distribution of the lifetimes to be Rayleigh distribution. An illustrative real data example and a simulation study are presented for the purpose of illustration and comparison of the proposed predictors.