Open AccessBayesian Inference for Reliability Function of Gompertz Distribution
Author(s) -
Huda A. Rasheed,
Tasnim H.K. Al-Baldawi,
Shurooq A K Al-Sultany
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1530/1/012054
Subject(s) - estimator , prior probability , bayes' theorem , mathematics , statistics , exponential distribution , statistical inference , bayesian probability , bayesian inference , function (biology) , bayes estimator , evolutionary biology , biology
In this paper, some Bayes estimators of the reliability function of Gompertz distribution have been derived based on generalized weighted loss function. In order to get a best understanding of the behaviour of Bayesian estimators, a non-informative prior as well as an informative prior represented by exponential distribution is considered. Monte-Carlo simulation have been employed to compare the performance of different estimates for the reliability function of Gompertz distribution based on Integrated mean squared errors. It was found that Bayes estimators with exponential prior information under the generalized weighted loss function were generally better than the estimators based on Jeffreys prior information.