Generalized Bayesian Inference Study Based on Type-II Censored Data from Pareto Distribution

Ghadah Alomani; Yahia Awad; Mohamed Kayid

doi:10.20944/preprints202409.0029.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 1 September 2024 / Approved: 2 September 2024 / Online: 2 September 2024 (08:54:10 CEST)

This paper investigates generalized Bayesian (GB) inference in the context of a one-parameter Pareto distribution. The study focuses on the derivation of generalized Bayesian estimators (GBE) and generalized empirical Bayesian estimators (GEBE) for the parameters of the Pareto distribution using type II censored data. In addition, the study investigates a one-sample prediction approach to examine generalized Bayesian prediction (GBP) and generalized empirical Bayesian prediction (GEBP). To evaluate the performance of these estimation and prediction methods, Monte Carlo simulations were performed comparing GBE with GEBE and GBP with GEBP for different parameter values and learning rate parameters (LRP).

Generalized Bayesian; learning rate parameter; Generalized Bayesian estimators; generalized empirical Bayesian estimators; generalized Bayesian prediction; generalized empirical Bayesian prediction; Simulation

Computer Science and Mathematics, Probability and Statistics

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Generalized Bayesian Inference Study Based on Type-II Censored Data from Pareto Distribution

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