Generalized Bayes Prediction Study Based on Joint Type-II Censoring

Autor:	Yahia Abdel-Aty, Mohamed Kayid, Ghadah Alomani
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	learning rate parameter exponential distribution joint type-II censoring point predictor prediction intervals squared-error loss Mathematics QA1-939
Zdroj:	Axioms, Vol 12, Iss 7, p 716 (2023)
Druh dokumentu:	article
ISSN:	2075-1680
DOI:	10.3390/axioms12070716
Popis:	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.
Databáze:	Directory of Open Access Journals
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