Van Niekerk, JanetBekker, Andriette, 1958-Arashi, Mohammad2018-03-192017-05Janet van Niekerk, Andriëtte Bekker & Mohammad Arashi (2017) A gamma-mixture class of distributions with Bayesian application, Communications in Statistics - Simulation and Computation, 46:10, 8152-8165, DOI: 10.1080/03610918.2016.1267754.0361-0918 (print)1532-4141 (online)10.1080/03610918.2016.1267754http://hdl.handle.net/2263/64298In 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.en© 2017 Taylor & Francis Group, LLC. This is an electronic version of an article published in Communications in Statistics : Simulation and Computation, vol. 46, no. 10, pp. 8152-8165, 2017. doi : 10.1080/03610918.2016.1267754. Communications in Statistics : Simulation and Computation is available online at : http://www.tandfonline.comloi/lssp20.Bayesian inferenceHypergeometric gammaMixture of gammaNormal-gammaNormal-inverse gammaVariancePostprint Article