Bayesian Inference for the Parameter and Reliability Function of Basic Gompertz Distribution under Precautionary loss Function | Zendy

Manahel Kh. Awad | Zendy; Huda A. Rasheed | Zendy

Open Access

Bayesian 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 -

ibn al- haitham journal for pure and applied science

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.

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