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Robust mixture regression using mean-shift penalisation
Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2021.
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| Other Authors: | |
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2022
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| _version_ | 1867613449205317632 |
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| access_status_str | Open Access |
| author2 | Kanfer, F.H.J. (Frans) |
| author_browse | Kanfer, F.H.J. (Frans) |
| author_facet | Kanfer, F.H.J. (Frans) |
| collection | Thesis |
| dc_rights_str_mv | © 2022 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 | Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2021. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/83624 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:19.431Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| 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:2263/83624 Robust mixture regression using mean-shift penalisation Kanfer, F.H.J. (Frans) anikawessels92@gmail.com Millard, Sollie M. Wessels, Anika UCTD Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2021. The purpose of finite mixture regression (FMR) is to model the relationship between a response and feature variables in the presence of latent groups in the population. The different regression structures are quantified by the unique parameters of each latent group. The Gaussian mixture regression model is a method commonly used in FMR since it simplifies the estimation and interpretation of the model output. However, it is highly affected if outliers are present in the data. Failing to account for the outliers may distort the results and lead to inappropriate conclusions. We consider a mean-shift robust mixture regression approach to address this. This method uses a component specific mean-shift parameterisation which contributes towards both the successful identification of outliers as well as robust parameter estimation. The technique is demonstrated by a simulation study and a real-world application. The mean-shift regression method proves to be highly robust against outliers. Statistics MSc (Advanced Data Analytics) Unrestricted 2022-02-04T08:09:00Z 2022-02-04T08:09:00Z 2022 2021 Mini Dissertation * A2022 http://hdl.handle.net/2263/83624 en © 2022 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 Robust mixture regression using mean-shift penalisation |
| title | Robust mixture regression using mean-shift penalisation |
| title_full | Robust mixture regression using mean-shift penalisation |
| title_fullStr | Robust mixture regression using mean-shift penalisation |
| title_full_unstemmed | Robust mixture regression using mean-shift penalisation |
| title_short | Robust mixture regression using mean-shift penalisation |
| title_sort | robust mixture regression using mean shift penalisation |
| topic | UCTD |
| url | http://hdl.handle.net/2263/83624 |