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A variance shilf model for outlier detection and estimation in linear and linear mixed models
Includes abstract.
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2014
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| _version_ | 1867613294444937216 |
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| access_status_str | Open Access |
| author | Gumedze, Freedom Nkhululeko |
| author_browse | Gumedze, Freedom Nkhululeko |
| author_facet | Gumedze, Freedom Nkhululeko |
| author_sort | Gumedze, Freedom Nkhululeko |
| collection | Thesis |
| description | Includes abstract. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/4381 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:51.607Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/4381 A variance shilf model for outlier detection and estimation in linear and linear mixed models Gumedze, Freedom Nkhululeko Statistical Sciences Includes abstract. Includes bibliographical references. Outliers are data observations that fall outside the usual conditional ranges of the response data.They are common in experimental research data, for example, due to transcription errors or faulty experimental equipment. Often outliers are quickly identified and addressed, that is, corrected, removed from the data, or retained for subsequent analysis. However, in many cases they are completely anomalous and it is unclear how to treat them. Case deletion techniques are established methods in detecting outliers in linear fixed effects analysis. The extension of these methods to detecting outliers in linear mixed models has not been entirely successful, in the literature. This thesis focuses on a variance shift outlier model as an approach to detecting and assessing outliers in both linear fixed effects and linear mixed effects analysis. A variance shift outlier model assumes a variance shift parameter, wi, for the ith observation, where wi is unknown and estimated from the data. Estimated values of wi indicate observations with possibly inflated variances relative to the remainder of the observations in the data set and hence outliers. When outliers lurk within anomalous elements in the data set, a variance shift outlier model offers an opportunity to include anomalies in the analysis, but down-weighted using the variance shift estimate wi. This down-weighting might be considered preferable to omitting data points (as in case-deletion methods). For very large values of wi a variance shift outlier model is approximately equivalent to the case deletion approach. 2014-07-30T17:44:02Z 2014-07-30T17:44:02Z 2008 Doctoral Thesis Doctoral Statistical Sciences http://hdl.handle.net/11427/4381 eng application/pdf Department of Statistical Sciences Faculty of Science University of Cape Town |
| spellingShingle | Statistical Sciences Gumedze, Freedom Nkhululeko A variance shilf model for outlier detection and estimation in linear and linear mixed models |
| thesis_degree_str | Doctoral |
| title | A variance shilf model for outlier detection and estimation in linear and linear mixed models |
| title_full | A variance shilf model for outlier detection and estimation in linear and linear mixed models |
| title_fullStr | A variance shilf model for outlier detection and estimation in linear and linear mixed models |
| title_full_unstemmed | A variance shilf model for outlier detection and estimation in linear and linear mixed models |
| title_short | A variance shilf model for outlier detection and estimation in linear and linear mixed models |
| title_sort | variance shilf model for outlier detection and estimation in linear and linear mixed models |
| topic | Statistical Sciences |
| url | http://hdl.handle.net/11427/4381 |
| work_keys_str_mv | AT gumedzefreedomnkhululeko avarianceshilfmodelforoutlierdetectionandestimationinlinearandlinearmixedmodels AT gumedzefreedomnkhululeko varianceshilfmodelforoutlierdetectionandestimationinlinearandlinearmixedmodels |