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Statistical inference on progressive‐stress accelerated life testing for the logistic exponential distribution under progressive type‐II censoring
Author(s) -
Kumar Mahto Amulya,
Dey Sanku,
Mani Tripathi Yogesh
Publication year - 2020
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2562
Subject(s) - censoring (clinical trials) , mathematics , accelerated life testing , exponential distribution , bayes' theorem , statistics , estimator , monte carlo method , confidence interval , statistical inference , scale parameter , exponential function , bayesian probability , weibull distribution , mathematical analysis
The accelerated life testing (ALT) is an efficient approach and has been used in several fields to obtain failure time data of test units in a much shorter time than testing at normal operating conditions. In this article, a progressive‐stress ALT under progressive type‐II censoring is considered when the lifetime of test units follows logistic exponential distribution. We assume that the scale parameter of the distribution satisfying the inverse power law. First, the maximum likelihood estimates of the model parameters and their approximate confidence intervals are obtained. Next, we obtain Bayes estimators under squared error loss function with the help of Metropolis‐Hasting (MH) algorithm. We also derive highest posterior density (HPD) credible intervals of the model parameters. Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation. Finally, one data set has been analyzed for illustrative purposes.