Title : ( A gamma-mixture class of distributions with Bayesian application )
Authors: Janet van Niekerk , Andriette Bekker , Mohammad Arashi ,
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Abstract
In this article, a subjective Bayesian approach is followed to derive estimators for the parameters of the normal model by assuming a gamma-mixture class of prior distributions, which includes the gamma and the noncentral gamma as special cases. An innovative approach is proposed to find the analytical expression of the posterior density function when a complicated prior structure is ensued. The simulation studies and a real dataset illustrate the modeling advantages of this proposed prior and support some of the findings.
Keywords
, Bayesian inference, Hypergeometric gamma, Mixture of gamma, Normal-gamma, Normal-inverse gamma, Variance
@article{paperid:1081568,
author = {Janet Van Niekerk and Andriette Bekker and Arashi, Mohammad},
title = {A gamma-mixture class of distributions with Bayesian application},
journal = {Communications in Statistics Part B: Simulation and Computation},
year = {2017},
volume = {46},
number = {10},
month = {November},
issn = {0361-0918},
pages = {8152--8165},
numpages = {13},
keywords = {Bayesian inference; Hypergeometric gamma; Mixture of gamma; Normal-gamma; Normal-inverse gamma; Variance},
}
%0 Journal Article
%T A gamma-mixture class of distributions with Bayesian application
%A Janet Van Niekerk
%A Andriette Bekker
%A Arashi, Mohammad
%J Communications in Statistics Part B: Simulation and Computation
%@ 0361-0918
%D 2017
