On Statistical Inference of Generalized Pareto Distribution with Jointly Progressive Censored Samples with Binomial Removal

A jointly censored sample is a very useful sampling technique in conducting comparative life tests of the products, its efficiency appears permitting selection two samples from manufacturing lines at same time and life-testing experiment. This article presents estimation information joint generalized Pareto distributions parameters using Type-II progressive censoring scheme, which carried out with binomial removal. The distribution has many applications different fields. We outline problem parameter frequentest maximum likelihood Bayesian methods. Furthermore, interval methods for estimating four were used: asymptotic property estimator, credible confidence intervals, Bootstrap intervals. detailed numerical simulations have been considered to compare performance proposed estimates. In addition, applicability model performed by applying real data example.