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arXiv · 2401.03081

Shrinkage Estimation and Prediction for Joint Type-II Censored Data from Two Burr-XII Populations

Soheila Akbari Bargoshadi·Hossein Bevrani
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Abstract

The main objective of this paper is to apply linear and pretest shrinkage estimation techniques to estimating the parameters of two 2-parameter Burr-XII distributions. Further more, predictions for future observations are made using both classical and Bayesian methods within a joint type-II censoring scheme. The efficiency of shrinkage estimates is compared to maximum likelihood and Bayesian estimates obtained through the expectation-maximization algorithm and importance sampling method, as developed by Akbari Bargoshadi et al. (2023) in "Statistical inference under joint type-II censoring data from two Burr-XII populations" published in Communications in Statistics-Simulation and Computation". For Bayesian estimations, both informative and non-informative prior distributions are considered. Additionally, various loss functions including squared error, linear-exponential, and generalized entropy are taken into account. Approximate confidence, credible, and highest probability density intervals are calculated. To evaluate the performance of the estimation methods, a Monte Carlo simulation study is conducted. Additionally, two real datasets are utilized to illustrate the proposed methods.

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Soheila Akbari Bargoshadi, Hossein Bevrani. 2024-01-05. Shrinkage Estimation and Prediction for Joint Type-II Censored Data from Two Burr-XII Populations. https://arxiv.org/abs/2401.03081

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