Matrix-variate beta generator - developments and application

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dc.contributor.author Van Niekerk, Janet
dc.contributor.author Bekker, Andriette, 1958-
dc.contributor.author Arashi, Mohammad
dc.date.accessioned 2022-08-30T12:23:56Z
dc.date.available 2022-08-30T12:23:56Z
dc.date.issued 2021-04-03
dc.description.abstract Matrix-variate beta distributions are applied in di erent fields of hypothesis testing, multivariate correlation analysis, zero regression, canonical correlation analysis and etc. Amethodology is proposed to generate matrix-variate beta generator distributions by combining the matrix-variate beta kernel with an unknown function of the trace operator. Several statistical characteristics, extensions and developments are presented. Special members are then used in a univariate and multivariate Bayesian analysis setting. These models are fitted to simulated and real datasets, and their fitting and performance are compared to well-established competitors. en_US
dc.description.department Statistics en_US
dc.description.librarian am2022 en_US
dc.description.sponsorship The National Research Foundation of South Africa en_US
dc.description.uri https://jirss.irstat.ir en_US
dc.identifier.citation Van Niekerk, J., Bekker, A. & Arasji, M. 2021, 'Matrix-variate beta generator - developments and application', JIRSS Journal of the Iranian Statistical Society, vol. 20, no. 1, pp. 289-306, doi : 10.52547/jirss.20.1.289. en_US
dc.identifier.issn 1726-4057 (print)
dc.identifier.issn 2538-189X (online)
dc.identifier.other 10.52547/jirss.20.1.289
dc.identifier.uri https://repository.up.ac.za/handle/2263/87008
dc.language.iso en en_US
dc.publisher Iranian Statistical Society en_US
dc.rights This work is licensed under a Creative Commons Attribution-NonCommercial 3.0. en_US
dc.subject Bayesian analysis en_US
dc.subject Binomial en_US
dc.subject Eigenvalues en_US
dc.subject Gaussian sample en_US
dc.subject Gibbs sampling en_US
dc.subject Matrix-variate beta en_US
dc.title Matrix-variate beta generator - developments and application en_US
dc.type Article en_US


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