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

• Authors: Muhammad Aslam , Mehreen Afzaal , M. Ishaq Bhatti

Abstracts

EN

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.

Keywords

EN

exponentiated Gompertz distribution; loss functions; informative priors; Bayes estimators; posterior risks

• Publisher: Główny Urząd Statystyczny
• Journal: Statistics in Transition new series
• Year: 2021
• Volume: 22
• Issue: 4
• Pages: 101-119

Contributors

author

Muhammad Aslam

• Department of Mathematics and Statistics, Riphah International University, Pakistan

• https://orcid.org/0000000333552330

author

Mehreen Afzaal

• Department of Mathematics and Statistics, Riphah International University, Pakistan

• https://orcid.org/0000000215503415

author

M. Ishaq Bhatti

• La Trobe Business School, La Trobe University, Australia

• https://orcid.org/0000000250277871

Identifiers

DOI

10.21307/stattrans-2021-040

Biblioteka Nauki

1917073