Reliability Estimation of XLindley Constant-Stress Partially Accelerated Life Tests using Progressively Censored Samples

It often takes a lot of time to conduct life-testing studies on products or components. Units can be tested under more severe circumstances than usual, known as accelerated life tests, reduce the testing period. This study’s goal is look into certain estimation issues related point and interval estimations for XLindley distribution constant stress partially tests with progressive Type-II censored samples. The maximum likelihood approach utilized acquire estimates model parameters well reliability function normal use conditions. Bayesian method using Monte Carlo Markov Chain procedure squared error loss also provided. Moreover, Bayes credible intervals highest posterior density different are considered. To make comparisons between proposed methods, simulation study conducted various sample sizes censoring schemes. usefulness suggested methodologies then demonstrated by analysis two data sets. A summary major findings found in conclusion.