Table of Contents
Journal of Quality and Reliability Engineering
Volume 2013, Article ID 530530, 8 pages
http://dx.doi.org/10.1155/2013/530530
Research Article

Inference on Reliability of Stress-Strength Models for Poisson Data

Department of Economics, Management and Quantitative Methods, University of Milan, 20122 Milan, Italy

Received 24 October 2012; Accepted 20 December 2012

Academic Editor: Shey-Huei Sheu

Copyright © 2013 Alessandro Barbiero. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Researchers in reliability engineering regularly encounter variables that are discrete in nature, such as the number of events (e.g., failures) occurring in a certain spatial or temporal interval. The methods for analyzing and interpreting such data are often based on asymptotic theory, so that when the sample size is not large, their accuracy is suspect. This paper discusses statistical inference for the reliability of stress-strength models when stress and strength are independent Poisson random variables. The maximum likelihood estimator and the uniformly minimum variance unbiased estimator are here presented and empirically compared in terms of their mean square error; recalling the delta method, confidence intervals based on these point estimators are proposed, and their reliance is investigated through a simulation study, which assesses their performance in terms of coverage rate and average length under several scenarios and for various sample sizes. The study indicates that the two estimators possess similar properties, and the accuracy of these estimators is still satisfactory even when the sample size is small. An application to an engineering experiment is also provided to elucidate the use of the proposed methods.