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

Muhammad Aslam, Mehreen Afzaal, M. Ishaq Bhatti

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.

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Year of publication:	2021
Authors:	Aslam, Muhammad ; Afzaal, Mehreen ; Bhatti, Muhammad Ishaq
Published in:	Statistics in transition : an international journal of the Polish Statistical Association and Statistics Poland. - Warszawa : GUS, ISSN 2450-0291, ZDB-ID 2235641-1. - Vol. 22.2021, 4, p. 101-119
Subject:	exponentiated Gompertz distribution \| loss functions \| informative priors \| Bayes estimators \| posterior risks \| Bayes-Statistik \| Bayesian inference \| Schätztheorie \| Estimation theory \| Statistische Verteilung \| Statistical distribution \| Wahrscheinlichkeitsrechnung \| Probability theory \| Informationsökonomik \| Economics of information