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Identifying outliers and influential observations in general linear regression models
Includes bibliographical references (leaves 140-149).
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2014
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| _version_ | 1867613305734955008 |
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| access_status_str | Open Access |
| author | Katshunga, Dominique |
| author2 | Troskie, Casper G |
| author_browse | Katshunga, Dominique Troskie, Casper G |
| author_facet | Troskie, Casper G Katshunga, Dominique |
| author_sort | Katshunga, Dominique |
| collection | Thesis |
| description | Includes bibliographical references (leaves 140-149). |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/6772 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:34:00.978Z |
| 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:11427/6772 Identifying outliers and influential observations in general linear regression models Katshunga, Dominique Troskie, Casper G Mathematical Statistics Includes bibliographical references (leaves 140-149). Identifying outliers and/or influential observations is a fundamental step in any statistical analysis, since their presence is likely to lead to erroneous results. Numerous measures have been proposed for detecting outliers and assessing the influence of observations on least squares regression results. Since outliers can arise in different ways, the above mentioned measures are based on motivational arguments and they are designed to measure the influence of observations on different aspects of various regression results. In what follows, we investigate how one can combine different test statistics based on residuals and diagnostic plots to identify outliers and influential observations (both in the single and multiple case) in general linear regression models. 2014-08-30T05:58:32Z 2014-08-30T05:58:32Z 2004 Thesis http://hdl.handle.net/11427/6772 eng application/pdf Department of Statistical Sciences Faculty of Science University of Cape Town |
| spellingShingle | Mathematical Statistics Katshunga, Dominique Identifying outliers and influential observations in general linear regression models |
| title | Identifying outliers and influential observations in general linear regression models |
| title_full | Identifying outliers and influential observations in general linear regression models |
| title_fullStr | Identifying outliers and influential observations in general linear regression models |
| title_full_unstemmed | Identifying outliers and influential observations in general linear regression models |
| title_short | Identifying outliers and influential observations in general linear regression models |
| title_sort | identifying outliers and influential observations in general linear regression models |
| topic | Mathematical Statistics |
| url | http://hdl.handle.net/11427/6772 |
| work_keys_str_mv | AT katshungadominique identifyingoutliersandinfluentialobservationsingenerallinearregressionmodels |