Bayesian Estimation of Marshall Olkin Extended Inverse Weibull Distribution Using MCMC Approach

Autor:	Hassan M. Okasha, A. H. El-Baz, Abdulkareem M. Basheer
Rok vydání:	2020
Předmět:	Bayes estimator Mean squared error Inverse Estimator Markov chain Monte Carlo Data application Statistics::Computation Bayes' theorem symbols.namesake symbols Statistics::Methodology Applied mathematics Mathematics Weibull distribution
Zdroj:	Journal of the Indian Society for Probability and Statistics. 21:247-257
ISSN:	2364-9569
DOI:	10.1007/s41096-020-00082-y
Popis:	In this paper, we invoke a new prospective to discuss the estimation of a three-parameter Marshall Olkin extended inverse Weibull distribution based on Markov Chain Monte Carlo (MCMC) approach. The Bayes estimators under the squared error loss and LINEX loss functions are derived for three parameters. MCMC approach is applied to compute the Bayesian estimation of the unknown parameters. Using a real data application, it is shown that the superior performance of Bayesian estimation.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3d7358b0bcb3a5182c423180c2bb5d1d https://doi.org/10.1007/s41096-020-00082-y Zobrazit plný text záznamu Full text from SpringerLink