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Generalized Bayes Prediction Study Based on Jointly Type-II Censored Sample from K-Exponential Populations
Version 1
: Received: 17 June 2023 / Approved: 19 June 2023 / Online: 19 June 2023 (05:12:04 CEST)
A peer-reviewed article of this Preprint also exists.
Abdel-Aty, Y.; Kayid, M.; Alomani, G. Generalized Bayes Prediction Study Based on Joint Type-II Censoring. Axioms 2023, 12, 716. Abdel-Aty, Y.; Kayid, M.; Alomani, G. Generalized Bayes Prediction Study Based on Joint Type-II Censoring. Axioms 2023, 12, 716.
Abstract
In this paper, the problem of predicting future failure times based on a jointly type-II censored sample from k exponential populations is considered. The Bayesian prediction intervals and point predictors were then obtained. Generalized Bayes is a Bayesian study based on a learning-rate parameter. This study investigated the effects of the learning rate parameters on the prediction results. The loss functions of squared error, Linex, and general entropy were used as point predictors. Monte Carlo simulations were performed to show the effectiveness of the learning rate parameter in improving the results of prediction intervals and point predictors.
Keywords
Generalized Bayes; learning rate parameter; exponential distribution; joint type-II censoring; point predictor; prediction intervals; squared-error loss; Linex loss; general entropy loss.
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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