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Semi-parametric mixtures of partially linear models
Mini Dissertation (MSc (eScience))--University of Pretoria, 2022.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2023
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| _version_ | 1867613537085423616 |
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| access_status_str | Open Access |
| author2 | Millard, Sollie M. |
| author_browse | Millard, Sollie M. |
| author_facet | Millard, Sollie M. |
| 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 (eScience))--University of Pretoria, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/89418 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:43.241Z |
| 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:2263/89418 Semi-parametric mixtures of partially linear models Millard, Sollie M. ruan@inflect.co.za Kanfer, F.H.J. (Frans) Du Randt, Ruan Jean EM algorithm Local kernel regression Non-parametric Profile likelihood Semi-parametric UCTD Mini Dissertation (MSc (eScience))--University of Pretoria, 2022. This mini-dissertation considers semi-parametric finite mixtures of partially linear models with Gaussian errors and focuses on the estimation procedure for such models. The semi-parametric structure allows for flexible modelling of the expected value of the response variable. These models are used in cases where the regression structure include both parametric and non-parametric covariate structures. We demonstrate the properties of the profile likelihood expectation maximisation algorithm (PL-EM) using a simulation study. The estimation algorithm is also demonstrated on real data. Overall, the estimation procedure is adequate in estimating the parameters of the mixtures of partially linear models from the results obtained in both the simulation study and the real-world application. DSI-NICIS National e-Science Postgraduate Teaching and Training Platform (NEPTTP) Statistics MSc (eScience) Unrestricted 2023-02-10T13:30:23Z 2023-02-10T13:30:23Z 2023 2022 Mini Dissertation * A2023 https://repository.up.ac.za/handle/2263/89418 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 | EM algorithm Local kernel regression Non-parametric Profile likelihood Semi-parametric UCTD Semi-parametric mixtures of partially linear models |
| title | Semi-parametric mixtures of partially linear models |
| title_full | Semi-parametric mixtures of partially linear models |
| title_fullStr | Semi-parametric mixtures of partially linear models |
| title_full_unstemmed | Semi-parametric mixtures of partially linear models |
| title_short | Semi-parametric mixtures of partially linear models |
| title_sort | semi parametric mixtures of partially linear models |
| topic | EM algorithm Local kernel regression Non-parametric Profile likelihood Semi-parametric UCTD |
| url | https://repository.up.ac.za/handle/2263/89418 |