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Modern variable selection techniques in the generalised linear model with application in Biostatistics
Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2020.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2020
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| _version_ | 1867613668994187264 |
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| access_status_str | Open Access |
| author2 | Arashi, Mohammad |
| author_browse | Arashi, Mohammad |
| author_facet | Arashi, Mohammad |
| 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 | Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2020. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/76508 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:39:48.717Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| 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:2263/76508 Modern variable selection techniques in the generalised linear model with application in Biostatistics Arashi, Mohammad u15176658@tuks.co.za Maribe, G. Millard, Salomi Mathematical statistics Penalised regression Feature selection UCTD Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2020. In a Biostatistics environment, the datasets to be analysed are frequently high-dimensional and multicollinearity is expected due to the nature of the features. However, many traditional approaches to statistical analysis and feature selection cease to be useful in the presence of high-dimensionality and multicollinearity. Penalised regression methods have proved to be practical and attractive for dealing with these problems. In this dissertation, we propose a new penalised approach, the modified elastic-net (MEnet), for statistical analysis and feature selection using a combination of the ridge and bridge penalties. This method is designed to deal with high-dimensional problems with highly correlated predictor variables. Furthermore, it has a closed-form solution, unlike the most frequently used penalised techniques, which makes it simple to implement on high-dimensional data. We show how this approach can be used to analyse high-dimensional data with binary responses, e.g., microarray data, and simultaneously select significant features. An extensive simulation study and analysis of a colon cancer dataset demonstrate the properties and practical aspects of the proposed method. DSI-CSIR Interbursary Support (IBS) Programme Statistics Industry HUB, Department of Statistics, University of Pretoria Statistics MSc Restricted 2020-10-16T11:54:45Z 2020-10-16T11:54:45Z 2021-04 2020-10 Mini Dissertation Millard, S 2020, Modern variable selection techniques in the generalised linear model with application in Biostatistics, MSc Mini Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/76508> http://hdl.handle.net/2263/76508 en © 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 | Mathematical statistics Penalised regression Feature selection UCTD Modern variable selection techniques in the generalised linear model with application in Biostatistics |
| title | Modern variable selection techniques in the generalised linear model with application in Biostatistics |
| title_full | Modern variable selection techniques in the generalised linear model with application in Biostatistics |
| title_fullStr | Modern variable selection techniques in the generalised linear model with application in Biostatistics |
| title_full_unstemmed | Modern variable selection techniques in the generalised linear model with application in Biostatistics |
| title_short | Modern variable selection techniques in the generalised linear model with application in Biostatistics |
| title_sort | modern variable selection techniques in the generalised linear model with application in biostatistics |
| topic | Mathematical statistics Penalised regression Feature selection UCTD |
| url | http://hdl.handle.net/2263/76508 |