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

Aslam Muhammad (), Afzaal Mehreen () and Ishaq Bhatti M. ()
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Aslam Muhammad: Department of Mathematics and Statistics, Riphah International University, Pakistan .
Afzaal Mehreen: Department of Mathematics and Statistics, Riphah International University, Pakistan .
Ishaq Bhatti M.: La Trobe Business School, La Trobe University, Australia .

Statistics in Transition New Series, 2021, vol. 22, issue 4, 101-119

Abstract: 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 real-life data in relation to simulated data. The empirical results of the comparison are presented through a graphical illustration of the posterior distributions.

Keywords: exponentiated Gompertz distribution; loss functions; informative priors; Bayes estimators; posterior risks. (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:22:y:2021:i:4:p:101-119:n:3

DOI: 10.21307/stattrans-2021-040

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