Abstract
Accelerated life testing is widely used in product life testing experiments since it provides significant reduction in time and cost of testing. In this article, we assume that the lifetime of items under use condition follows the two-parameter distributions having power hazard function, and partially accelerated life tests based on progressive type-II censored samples are considered. The maximum likelihood, Bayes, and parametric bootstrap methods are used for estimating the unknown parameters. Based on normal approximation to the asymptotic distribution of MLEs, the approximate confidence intervals for the parameters are derived. Two bootstrap confidence intervals are also proposed. The classical Bayes estimates cannot be obtained in explicit form, so we propose to apply the Markov chain Monte Carlo (MCMC) technique. Gibbs within the Metropolis–Hasting algorithm has been applied to generate MCMC samples from the posterior density function. Based on the generated samples, the Bayes estimates and the highest posterior density credible intervals of the unknown parameters have been computed. Finally, analysis of a simulated data set has also been presented to illustrate the proposed estimation methods developed here.
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