A gamma-mixture class of distributions with Bayesian application
| dc.contributor.author | Van Niekerk, Janet | |
| dc.contributor.author | Bekker, Andriette, 1958- | |
| dc.contributor.author | Arashi, Mohammad | |
| dc.contributor.email | janet.vanniekerk@up.ac.za | en_ZA |
| dc.date.accessioned | 2018-03-19T05:57:09Z | |
| dc.date.issued | 2017-05 | |
| dc.description.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. | en_ZA |
| dc.description.department | Statistics | en_ZA |
| dc.description.embargo | 2018-05-24 | |
| dc.description.librarian | hj2018 | en_ZA |
| dc.description.sponsorship | The StatDisT group. This work is based upon research supported by the National Research foundation, Grant (Re:CPRR13090132066 No 91497) and the vulnerable discipline-academic statistics (STAT) fund. | en_ZA |
| dc.description.uri | http://www.tandfonline.com/loi/lssp20 | en_ZA |
| dc.identifier.citation | Janet 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. | en_ZA |
| dc.identifier.issn | 0361-0918 (print) | |
| dc.identifier.issn | 1532-4141 (online) | |
| dc.identifier.other | 10.1080/03610918.2016.1267754 | |
| dc.identifier.uri | http://hdl.handle.net/2263/64298 | |
| dc.language.iso | en | en_ZA |
| dc.publisher | Taylor and Francis | en_ZA |
| dc.rights | © 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. | en_ZA |
| dc.subject | Bayesian inference | en_ZA |
| dc.subject | Hypergeometric gamma | en_ZA |
| dc.subject | Mixture of gamma | en_ZA |
| dc.subject | Normal-gamma | en_ZA |
| dc.subject | Normal-inverse gamma | en_ZA |
| dc.subject | Variance | en_ZA |
| dc.title | A gamma-mixture class of distributions with Bayesian application | en_ZA |
| dc.type | Postprint Article | en_ZA |