Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical
Hamdy, A., Almetwally, E. (2023). Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical. EKB Journal Management System, 2(1), 31-51. doi: 10.21608/cjmss.2023.185497.1001
Ahlam Hamdy; Ehab M. Almetwally. "Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical". EKB Journal Management System, 2, 1, 2023, 31-51. doi: 10.21608/cjmss.2023.185497.1001
Hamdy, A., Almetwally, E. (2023). 'Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical', EKB Journal Management System, 2(1), pp. 31-51. doi: 10.21608/cjmss.2023.185497.1001
Hamdy, A., Almetwally, E. Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical. EKB Journal Management System, 2023; 2(1): 31-51. doi: 10.21608/cjmss.2023.185497.1001

Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical

Computational Journal of Mathematical and Statistical Sciences
Volume 2, Issue 1 - Serial Number 2, April 2023, Page 31-51  XML PDF (646.29 K)
Document Type: Original Article
DOI: 10.21608/cjmss.2023.185497.1001
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Authors
Ahlam Hamdy email orcid 1; Ehab M. Almetwally2
1Mansoura Mansoura
2Department of Statistics, Faculty of Business Administration, Delta University for Science and Technology, Gamasa 11152, Egypt
Abstract
Generalized power Akshaya distribution is a brand-new two-parameter distribution that builds on the Akshaya distribution first introduced by \cite{Ramadan}. The lifetime data is intended to be modelled by this distribution. The generalised power Akshaya's parameters are estimated using both the conventional and Bayesian approaches in this work. The weighted least square estimation (WLSE), least square estimation (LSE), Cramer-von-Mises estimation (CVME), Anderson and Darling (AD) method of estimation, Maximum Product Space Estimators (MPSE), and maximum likelihood estimation (MLE), six traditional estimation methods, are used to find the model parameters. The parameters of the suggested distribution were also determined using the squared error loss function and Bayesian estimating (BE) under independent gamma priors. The unknown parameters have been estimated using the Bayesian approach using Markov chain Monte Carlo (MCMC). Additionally, the parameters' average width of the confidence intervals and coverage probability are computed. Additionally, the reliable intervals for Bayesian estimates of the unknown parameters calculated.
Keywords
Generalized power Akshaya; Bayesian procedure; Weighted least square; Maximum Product Spacing; Markov chain Monte Carlo
Statistics
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