On A Shape Parameter of Gompertz Inverse Exponential Distribution Using Classical and Non Classical Methods of Estimation

Main Article Content

Terna Godfrey Ieren
Adana’a Felix Chama
Olateju Alao Bamigbala
Jerry Joel
Felix M. Kromtit
Innocent Boyle Eraikhuemen

Abstract

The Gompertz inverse exponential distribution is a three-parameter lifetime model with greater flexibility and performance for analyzing real life data. It has one scale parameter and two shape parameters responsible for the flexibility of the distribution. Despite the importance and necessity of parameter estimation in model fitting and application, it has not been established that a particular estimation method is better for any of these three parameters of the Gompertz inverse exponential distribution. This article focuses on the development of Bayesian estimators for a shape of the Gompertz inverse exponential distribution using two non-informative prior distributions (Jeffery and Uniform) and one informative prior distribution (Gamma prior) under Square error loss function (SELF), Quadratic loss function (QLF) and Precautionary loss function (PLF). These results are compared with the maximum likelihood counterpart using Monte Carlo simulations. Our results indicate that Bayesian estimators under Quadratic loss function (QLF) with any of the three prior distributions provide the smallest mean square error for all sample sizes and different values of parameters.

Keywords:
Bayesian method, uniform prior, Jeffrey’s prior, gamma prior, loss functions, MLE, MSE, sample sizes.

Article Details

How to Cite
Godfrey Ieren, T., Felix Chama, A., Alao Bamigbala, O., Joel, J., M. Kromtit, F., & Boyle Eraikhuemen, I. (2020). On A Shape Parameter of Gompertz Inverse Exponential Distribution Using Classical and Non Classical Methods of Estimation. Journal of Scientific Research and Reports, 25(6), 1-10. https://doi.org/10.9734/jsrr/2019/v25i630203
Section
Original Research Article

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