Version 1
: Received: 2 August 2023 / Approved: 4 August 2023 / Online: 4 August 2023 (08:43:11 CEST)
How to cite:
Abdel-Aty, Y.; Kayid, M.; Alomani, G. Bayesian Estimation Based on Learning Rate Parameter Under the Joint Hybrid Censoring Scheme for K Exponential Populations. Preprints2023, 2023080409. https://doi.org/10.20944/preprints202308.0409.v1
Abdel-Aty, Y.; Kayid, M.; Alomani, G. Bayesian Estimation Based on Learning Rate Parameter Under the Joint Hybrid Censoring Scheme for K Exponential Populations. Preprints 2023, 2023080409. https://doi.org/10.20944/preprints202308.0409.v1
Abdel-Aty, Y.; Kayid, M.; Alomani, G. Bayesian Estimation Based on Learning Rate Parameter Under the Joint Hybrid Censoring Scheme for K Exponential Populations. Preprints2023, 2023080409. https://doi.org/10.20944/preprints202308.0409.v1
APA Style
Abdel-Aty, Y., Kayid, M., & Alomani, G. (2023). Bayesian Estimation Based on Learning Rate Parameter Under the Joint Hybrid Censoring Scheme for K Exponential Populations. Preprints. https://doi.org/10.20944/preprints202308.0409.v1
Chicago/Turabian Style
Abdel-Aty, Y., Mohamed Kayid and Ghadah Alomani. 2023 "Bayesian Estimation Based on Learning Rate Parameter Under the Joint Hybrid Censoring Scheme for K Exponential Populations" Preprints. https://doi.org/10.20944/preprints202308.0409.v1
Abstract
Generalized Bayes is a Bayesian approach based on the learning rate parameter η. In this study, we examine the effect of parameter η on the estimation results considering joint type-I and type-II hybrid censored samples from k exponential populations. In addition to the learning rate parameter, we consider two loss functions, the Linex and general entropy loss functions in the Bayesian approach. Monte Carlo simulations are performed to compare the performances of the estimation results under losses and different values of η. An illustrative example is performed to study the effect of the learning rate parameter and the different losses with different parameters.
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.
