A Family of Bayes Estimators for the Parameters of the Generalized Gamma Distribution

The paper aims to propose a family of estimators for the Bayesian analysis of three parametric generalized gamma (GG) distribution under different priors and loss functions. We have proposed the Gibbs sampler to obtain the numerical solutions for the point and interval estimators of the parameters using WinBugs. The comparison among the different estimators has been made in terms of posterior risks and the widths of the corresponding credible intervals. A simulation study has been conducted to investigate the performance of the estimators under different combinations of the parametric values and using various sample sizes. A real life data set has been analyzed to illustrate the practical applicability of the results