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

Abstract

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