Bayesian Inference and Prediction of Burr Type XII Distribution for Progressive First Failure Censored Sampling

Autor:	A. H. Abd Ellah, N. A. Abou-Elheggag, A. A. Modhesh, Ahmed A. Soliman
Rok vydání:	2011
Předmět:	Bayes' theorem symbols.namesake Burr distribution Statistics symbols Prediction interval Markov chain Monte Carlo Bayesian inference Bayesian linear regression Importance sampling Statistics::Computation Mathematics Gibbs sampling
Zdroj:	Intelligent Information Management. :175-185
ISSN:	2160-5920 2160-5912
DOI:	10.4236/iim.2011.35021
Popis:	This paper deals with Bayesian inference and prediction problems of the Burr type XII distribution based on progressive first failure censored data. We consider the Bayesian inference under a squared error loss function. We propose to apply Gibbs sampling procedure to draw Markov Chain Monte Carlo (MCMC) samples, and they have in turn, been used to compute the Bayes estimates with the help of importance sampling technique. We have performed a simulation study in order to compare the proposed Bayes estimators with the maximum likelihood estimators. We further consider two sample Bayes prediction to predicting future order statistics and upper record values from Burr type XII distribution based on progressive first failure censored data. The predictive densities are obtained and used to determine prediction intervals for unobserved order statistics and upper record values. A real life data set is used to illustrate the results derived.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6e84040c6b1af0670c1d4e2a020c9986 https://doi.org/10.4236/iim.2011.35021 Zobrazit plný text záznamu