Version 1
: Received: 17 August 2024 / Approved: 2 September 2024 / Online: 3 September 2024 (02:50:04 CEST)
How to cite:
Abdel-Aty, Y.; Kayid, M.; Alomani, G. Generalized Bayesian Inference Study Based on Type-II Censored Data from the Class of Exponential Distributions. Preprints2024, 2024090087. https://doi.org/10.20944/preprints202409.0087.v1
Abdel-Aty, Y.; Kayid, M.; Alomani, G. Generalized Bayesian Inference Study Based on Type-II Censored Data from the Class of Exponential Distributions. Preprints 2024, 2024090087. https://doi.org/10.20944/preprints202409.0087.v1
Abdel-Aty, Y.; Kayid, M.; Alomani, G. Generalized Bayesian Inference Study Based on Type-II Censored Data from the Class of Exponential Distributions. Preprints2024, 2024090087. https://doi.org/10.20944/preprints202409.0087.v1
APA Style
Abdel-Aty, Y., Kayid, M., & Alomani, G. (2024). Generalized Bayesian Inference Study Based on Type-II Censored Data from the Class of Exponential Distributions. Preprints. https://doi.org/10.20944/preprints202409.0087.v1
Chicago/Turabian Style
Abdel-Aty, Y., Mohamed Kayid and Ghadah Alomani. 2024 "Generalized Bayesian Inference Study Based on Type-II Censored Data from the Class of Exponential Distributions" Preprints. https://doi.org/10.20944/preprints202409.0087.v1
Abstract
Generalized Bayesian (GB) is a Bayesian approach based on the learning rate parameter (LRP) () as a fraction of the power of the likelihood function. In this paper, we consider the GB method to perform inference studies for a class of exponential distributions. Generalized Bayesian estimators (GBE) and generalized empirical Bayesian estimators (GEBE) for the parameters of the considered distributions were obtained based on the censored type II samples. In addition, generalized Bayesian prediction (GBP) and generalized empirical Bayesian prediction (GEBP) were considered using a one-sample prediction scheme. Monte Carlo simulations were performed to compare the performance of the GBE and GEBE estimation results and the GBP and GEBP prediction results for different values of the LRP.
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.
