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Developments in Wishart ensemble and Bayesian application
Thesis (PhD (Mathematical Statistics))--University of Pretoria, 2017.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2023
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| _version_ | 1867613545545334785 |
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| access_status_str | Open Access |
| author2 | Bekker, Andriette, 1958- |
| author_browse | Bekker, Andriette, 1958- |
| author_facet | Bekker, Andriette, 1958- |
| collection | Thesis |
| dc_rights_str_mv | © 2021 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Thesis (PhD (Mathematical Statistics))--University of Pretoria, 2017. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/93806 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:51.329Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/93806 Developments in Wishart ensemble and Bayesian application Bekker, Andriette, 1958- Arashi, Mohammad Van Niekerk, Janet UCTD Wishart ensemble Bayesian application Algorithms Bayesian analysis Thesis (PhD (Mathematical Statistics))--University of Pretoria, 2017. The increased complexity and dimensionality of data necessitates the development of new models that can adequately model the data. Advances in computational approaches have pathed the way for consideration and implementation of more complicated models, previously avoided due to practical difficulties. New models within theWishart ensemble are developed and some properties are derived. Algorithms for the practical implementation of these matrix variate models are proposed. Simulation studies and real datasets are used to illustrate the use and improved performance of these new models in Bayesian analysis of the multivariate and univariate normal models. From this speculative research study the following papers emanated: 1. J. Van Niekerk, A. Bekker, M. Arashi, and J.J.J. Roux (2015). “Subjective Bayesian analysis of the elliptical model”. In: Communications in Statistics - Theory and Methods 44.17, 3738–3753 2. J. Van Niekerk, A. Bekker, M. Arashi, and D.J. De Waal (2016). “Estimation under the matrix variate elliptical model”. In: South African Statistical Journal 50.1, 149–171 3. J. Van Niekerk, A. Bekker, and M. Arashi (2016). “A gamma-mixture class of distributions with Bayesian application”. In: Communications in Statistics - Simulation and Computation (Accepted) 4. M. Arashi, A. Bekker, and J. Van Niekerk (2017). “Weighted-type Wishart distributions with application”. In: Revstat 15(2), 205–222 5. A. Bekker, J. Van Niekerk, and M. Arashi (2017). “Wishart distributions - Advances in Theory with Bayesian application”. In: Journal of Multivariate Analysis 155, 272–283 Statistics PhD (Mathematical Statistics) Unrestricted Faculty of Natural and Agricultural Sciences 2023-12-19T09:04:27Z 2023-12-19T09:04:27Z 2018 2017 Thesis * A2018 http://hdl.handle.net/2263/93806 en © 2021 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Wishart ensemble Bayesian application Algorithms Bayesian analysis Developments in Wishart ensemble and Bayesian application |
| title | Developments in Wishart ensemble and Bayesian application |
| title_full | Developments in Wishart ensemble and Bayesian application |
| title_fullStr | Developments in Wishart ensemble and Bayesian application |
| title_full_unstemmed | Developments in Wishart ensemble and Bayesian application |
| title_short | Developments in Wishart ensemble and Bayesian application |
| title_sort | developments in wishart ensemble and bayesian application |
| topic | UCTD Wishart ensemble Bayesian application Algorithms Bayesian analysis |
| url | http://hdl.handle.net/2263/93806 |