Generalized Bayes Estimation Based on a Joint Type-II Censored Sample from K-Exponential Populations

Yahia Abdel-Aty, Mohamed Kayid () and Ghadah Alomani
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Yahia Abdel-Aty: Department of Mathematics, College of Science, Taibah University, Al-Madinah Al-Munawarah 30002, Saudi Arabia
Mohamed Kayid: Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Ghadah Alomani: Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Mathematics, 2023, vol. 11, issue 9, 1-11

Abstract: Generalized Bayes is a Bayesian study based on a learning rate parameter. This paper considers a generalized Bayes estimation to study the effect of the learning rate parameter on the estimation results based on a joint censored sample of type-II exponential populations. Squared error, Linex, and general entropy loss functions are used in the Bayesian approach. Monte Carlo simulations were performed to assess how well the different approaches perform. The simulation study compares the Bayesian estimators for different values of the learning rate parameter and different losses.

Keywords: generalized bayes; learning rate parameter; exponential distribution; joint type-II censoring; squared-error loss; Linex loss; general entropy loss (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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