A study on exponentiated Gompertz distribution under Bayesian discipline using informative priors


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Statistics in Transition New Series

Polish Statistical Association

Central Statistical Office of Poland

Subject: Economics, Statistics & Probability


ISSN: 1234-7655
eISSN: 2450-0291





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VOLUME 22 , ISSUE 4 (December 2021) > List of articles

A study on exponentiated Gompertz distribution under Bayesian discipline using informative priors

Muhammad Aslam / Mehreen Afzaal / M. Ishaq Bhatti *

Keywords : exponentiated Gompertz distribution, loss functions, informative priors, Bayes estimators, posterior risks

Citation Information : Statistics in Transition New Series. Volume 22, Issue 4, Pages 101-119, DOI: https://doi.org/10.21307/stattrans-2021-040

License : (CC BY-NC-ND 4.0)

Received Date : 26-August-2020 / Accepted: 16-February-2021 / Published Online: 08-December-2021



The exponentiated Gompertz (EGZ) distribution has been recently used in almost all areas of human endeavours, starting from modelling lifetime data to cancer treatment. Various applications and properties of the EGZ distribution are provided by Anis and De (2020). This paper explores the important properties of the EGZ distribution under Bayesian discipline using two informative priors: the Gamma Prior (GP) and the Inverse Levy Prior (ILP). This is done in the framework of five selected loss functions. The findings show that the two best loss functions are the Weighted Balance Loss Function (WBLF) and the Quadratic Loss Function (QLF). The usefulness of the model is illustrated by the use of reallife data in relation to simulated data. The empirical results of the comparison are presented through a graphical illustration of the posterior distributions.

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