A new lifetime distribution for a series-parallel system: properties, applications and estimations under progressive type-II censoring. Issue 5 (24th March 2017)

• Record Type: Journal Article
• Authors: Abdel-Hamid, Alaa H.; Hashem, Atef F.
• Is Part Of: Journal of statistical computation and simulation. Volume 87:Issue 5(2017)
• Page Start: 993
• Page End: 1024
• Publication Date: 2017-03-24
• Subjects: Exponential distribution -- maximum likelihood estimation -- moments estimation -- least squares estimation -- weighted least squares estimation -- Bayes estimation; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2016.1243683
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: A compound class of zero truncated Poisson and lifetime distributions is introduced. A specialization is paved to a new three-parameter distribution, called doubly Poisson-exponential distribution, which may represent the lifetime of units connected in a series-parallel system. The new distribution can be obtained by compounding two zero truncated Poisson distributions with an exponential distribution. Among its motivations is that its hazard rate function can take different shapes such as decreasing, increasing and upside-down bathtub depending on the values of its parameters. Several properties of the new distribution are discussed. Based on progressive type-II censoring, six estimation methods [maximum likelihood, moments, least squares, weighted least squares and Bayes (under linear-exponential and general entropy loss functions) estimations] are used to estimate the involved parameters. The performance of these methods is investigated through a simulation study. The Bayes estimates are obtained using Markov chain Monte Carlo algorithm. In addition, confidence intervals, symmetric credible intervals and highest posterior density credible intervals of the parameters are obtained. Finally, an application to a real data set is used to compare the new distribution with other five distributions.