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Bayesian inference for Rayleigh distribution under progressive censored sample
Author(s) -
Wu ShuoJye,
Chen DarHsin,
Chen ShyiTien
Publication year - 2006
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.615
Subject(s) - rayleigh distribution , estimator , bayesian probability , statistics , bayesian inference , bayes' theorem , bayes factor , inference , posterior probability , posterior predictive distribution , mathematics , statistical inference , computer science , sample (material) , bayesian linear regression , econometrics , probability density function , artificial intelligence , chemistry , chromatography
It is often the case that some information is available on the parameter of failure time distributions from previous experiments or analyses of failure time data. The Bayesian approach provides the methodology for incorporation of previous information with the current data. In this paper, given a progressively type II censored sample from a Rayleigh distribution, Bayesian estimators and credible intervals are obtained for the parameter and reliability function. We also derive the Bayes predictive estimator and highest posterior density prediction interval for future observations. Two numerical examples are presented for illustration and some simulation study and comparisons are performed. Copyright © 2006 John Wiley & Sons, Ltd.