Classical and Bayesian Inference under Burr-X Distribution Based on New Unified Progressive Hybrid Censoring Scheme with Engineering Applications

Abstract

In this paper, a new unified progressive hybrid censoring scheme $\left(\text{UPHCS}\right)$ has been constructed. This unified censoring scheme covers eleven famous censoring schemes. The estimation problem of Burr-X distribution parameters has been studied using the maximum likelihood and Bayes approaches based on the suggested unified progressive hybrid censored samples. Two real data sets have been used as illustrative engineering examples.