Inference and prediction of progressive Type-II censored data from Unit-Generalized Rayleigh distribution

Abstract

In this paper, inference and prediction problems are studied under progressively Type-II censored data. When the latent lifetime follows the Unit-Generalized Rayleigh distribution, maximum likelihood estimators of the unknown parameters are established, and corresponding existence and uniqueness are also provided. Besides, the approximate confidence intervals are constructed based on asymptotic approximation theory. For comparison, another alternative generalized point and interval estimates are constructed based on proposed pivotal quantities. Further, point and interval predictions for the censored samples are established by using conventional classical and generalized inferential approaches. Finally, extensive simulation studies are carried out to investigate the performance of different methods, and one real-life example is presented to illustrate the applicability of the obtained results.