Bayes estimation for exponential distributions with common location parameter and applications to multi-state reliability models. Issue 15 (17th November 2016)

• Record Type: Journal Article
• Authors: Jana, Nabakumar; Kumar, Somesh; Chatterjee, Kashinath
• Is Part Of: Journal of applied statistics. Volume 43:Issue 15(2016)
• Page Start: 2697
• Page End: 2712
• Publication Date: 2016-11-17
• Subjects: Bayes estimator -- uniformly minimum variance unbiased estimator -- maximum likelihood estimator -- best scale equivariant estimator -- stress–strength model; 62F10 -- 62N05; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2016.1142950
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: This paper considers the estimation of the stress–strength reliability of a multi-state component or of a multi-state system where its states depend on the ratio of the strength and stress variables through a kernel function. The article presents a Bayesian approach assuming the stress and strength as exponentially distributed with a common location parameter but different scale parameters. We show that the limits of the Bayes estimators of both location and scale parameters under suitable priors are the maximum likelihood estimators as given by Ghosh and Razmpour [15 ]. We use the Bayes estimators to determine the multi-state stress–strength reliability of a system having states between 0 and 1. We derive the uniformly minimum variance unbiased estimators of the reliability function. Interval estimation using the bootstrap method is also considered. Under the squared error loss function and linex loss function, risk comparison of the reliability estimators is carried out using extensive simulations.