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Computational aspects of hierarchical mixture models for geological data
Dissertation (MSc (Mathematical Statistics))--University of Pretoria, 2021.
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| Format: | Thesis |
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University of Pretoria
2021
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| _version_ | 1867613609699311616 |
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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 | © 2019 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 | Dissertation (MSc (Mathematical Statistics))--University of Pretoria, 2021. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/78378 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:38:52.535Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| 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:2263/78378 Computational aspects of hierarchical mixture models for geological data Bekker, Andriette, 1958- m.laidlaw17@gmail.com Ferreira, Johan T. Laidlaw, Michaela UCTD Dissertation (MSc (Mathematical Statistics))--University of Pretoria, 2021. Buidling off a foundation of knowledge from studies into modelling wind speed, models are fitted to multimodal datasets of geological nature. Mixtures of distributions are derived with parameter updates done via implementation of the EM algorithm. Among these mixtures is the Birnbaum-Saunders which is used as a component of hierarchical mixture of multiple distributions for the first time. The derivations of parameter updates in the EM algorithm setting is done and application to five real world datasets, one of which is large, implemented whilst keeping computation in mind. Additionally a simulation study is done for the mixtures of distributions with results indicating larger samples result in better fit whilst not compromising runtimes. Simulation studies for hierarcical mixtures to be considered in future work as obtaining convergence is challenging. SRUG190308422768 grant No. 120839 and NRF GRANT : VULNERABLE DISCIPLINE: ACADEMIC STATISTICS (STATS). Statistics MSc (Mathematical Statistics) Restricted 2021-02-10T08:22:20Z 2021-02-10T08:22:20Z 2021-04 2021 Dissertation * A2021 http://hdl.handle.net/2263/78378 © 2019 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 Computational aspects of hierarchical mixture models for geological data |
| title | Computational aspects of hierarchical mixture models for geological data |
| title_full | Computational aspects of hierarchical mixture models for geological data |
| title_fullStr | Computational aspects of hierarchical mixture models for geological data |
| title_full_unstemmed | Computational aspects of hierarchical mixture models for geological data |
| title_short | Computational aspects of hierarchical mixture models for geological data |
| title_sort | computational aspects of hierarchical mixture models for geological data |
| topic | UCTD |
| url | http://hdl.handle.net/2263/78378 |