Bayesian estimation of the parameter of a generalized geometric series distribution under different prior distributions and loss functions

Abstract

Comparisons of estimates between the Bayes and frequentist methods are fascinating and challenging theme of interest in statistics with significant impact on professionals. For Bayes estimators, the performance depends on the form of the prior distribution and the assumed loss function. This paper illustrates the problem of estimation of the one-parameter generalized geometric series distribution, using conjugate and improper prior distributions under symmetric and asymmetric loss functions. Performance of the Bayes estimates with respect to different priors, loss functions, and maximum likelihood estimates are illustrated for a data set and through a simulation study.