Open AccessBayesian Inference for the Parameter and Reliability Function of Basic Gompertz Distribution under Precautionary loss Function
Author(s) -
Manahel Kh. Awad,
Huda A. Rasheed
Publication year - 2020
Publication title -
mağallaẗ ibn al-haytam li-l-ʻulūm al-ṣirfaẗ wa-al-taṭbīqiyyaẗ/ibn al-haitham journal for pure and applied sciences
Language(s) - English
Resource type - Journals
eISSN - 2521-3407
pISSN - 1609-4042
DOI - 10.30526/33.2.2435
Subject(s) - estimator , mean squared error , mathematics , statistics , prior probability , bayesian probability , reliability (semiconductor) , bayes estimator , function (biology) , gompertz function , bayesian inference , power (physics) , physics , quantum mechanics , evolutionary biology , biology
In this paper, some estimators for the unknown shape parameter and reliability function of Basic Gompertz distribution have been obtained, such as Maximum likelihood estimator and Bayesian estimators under Precautionary loss function using Gamma prior and Jefferys prior. Monte-Carlo simulation is conducted to compare mean squared errors (MSE) for all these estimators for the shape parameter and integrated mean squared error (IMSE's) for comparing the performance of the Reliability estimators. Finally, the discussion is provided to illustrate the results that summarized in tables.