Robust estimation in familial and longitudinal models
Thesis (Ph.D.)--Memorial University of Newfoundland, 2007. Mathematics and Statistics Includes bibliographical references (leaves 141-144) There exists many studies on the robust estimation of the regression effects in a linear model set up for continuous such as Gaussian data possibly containing on...
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ftmemorialunivdc:oai:collections.mun.ca:theses4/82127 2023-05-15T17:23:34+02:00 Robust estimation in familial and longitudinal models Bari, Wasimul, 1972- Memorial University of Newfoundland. Dept. of Mathematics and Statistics 2007 xii,144 leaves Image/jpeg; Application/pdf http://collections.mun.ca/cdm/ref/collection/theses4/id/82127 Eng eng Electronic Theses and Dissertations (16.31 MB) -- http://collections.mun.ca/PDFs/theses/Bari_Wasimul2.pdf a2562080 http://collections.mun.ca/cdm/ref/collection/theses4/id/82127 The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries Estimation theory Linear models (Statistics) Regression analysis Robust statistics Text Electronic thesis or dissertation 2007 ftmemorialunivdc 2015-08-06T19:22:11Z Thesis (Ph.D.)--Memorial University of Newfoundland, 2007. Mathematics and Statistics Includes bibliographical references (leaves 141-144) There exists many studies on the robust estimation of the regression effects in a linear model set up for continuous such as Gaussian data possibly containing one or more outliers. The robust estimation of the regression effects in a generalized linear model (GLM) set up for the count and binary data in the presence of outliers is, however, relatively difficult. In this thesis, we deal with this difficult estimation issue and develop the robust estimation procedures under three scenarios. First, a fully standardized Mallows-type quasi-likelihood (FSMQL) estimation technique is developed to obtain consistent regression estimates in the GLM set up for both independent count and binary data. Secondly, we develop a robust generalized quasi-likelihood (RGQL) estimation procedure to deal with the outliers in the generalized linear mixed model (GLMM) set up for both count and binary data. Finally, we also develop the RGQL estimation procedure to deal with possible outliers in the GLM set up for the longitudinal count and binary data. The performances of the proposed robust estimators are examined through extensive simulation studies under all three set up: the GLM for the independent count and binary data; the GLMM for the familial count and binary data; and the GLM for the longitudinal count and binary data. Thesis Newfoundland studies University of Newfoundland Memorial University of Newfoundland: Digital Archives Initiative (DAI) |
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Memorial University of Newfoundland: Digital Archives Initiative (DAI) |
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English |
topic |
Estimation theory Linear models (Statistics) Regression analysis Robust statistics |
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Estimation theory Linear models (Statistics) Regression analysis Robust statistics Bari, Wasimul, 1972- Robust estimation in familial and longitudinal models |
topic_facet |
Estimation theory Linear models (Statistics) Regression analysis Robust statistics |
description |
Thesis (Ph.D.)--Memorial University of Newfoundland, 2007. Mathematics and Statistics Includes bibliographical references (leaves 141-144) There exists many studies on the robust estimation of the regression effects in a linear model set up for continuous such as Gaussian data possibly containing one or more outliers. The robust estimation of the regression effects in a generalized linear model (GLM) set up for the count and binary data in the presence of outliers is, however, relatively difficult. In this thesis, we deal with this difficult estimation issue and develop the robust estimation procedures under three scenarios. First, a fully standardized Mallows-type quasi-likelihood (FSMQL) estimation technique is developed to obtain consistent regression estimates in the GLM set up for both independent count and binary data. Secondly, we develop a robust generalized quasi-likelihood (RGQL) estimation procedure to deal with the outliers in the generalized linear mixed model (GLMM) set up for both count and binary data. Finally, we also develop the RGQL estimation procedure to deal with possible outliers in the GLM set up for the longitudinal count and binary data. The performances of the proposed robust estimators are examined through extensive simulation studies under all three set up: the GLM for the independent count and binary data; the GLMM for the familial count and binary data; and the GLM for the longitudinal count and binary data. |
author2 |
Memorial University of Newfoundland. Dept. of Mathematics and Statistics |
format |
Thesis |
author |
Bari, Wasimul, 1972- |
author_facet |
Bari, Wasimul, 1972- |
author_sort |
Bari, Wasimul, 1972- |
title |
Robust estimation in familial and longitudinal models |
title_short |
Robust estimation in familial and longitudinal models |
title_full |
Robust estimation in familial and longitudinal models |
title_fullStr |
Robust estimation in familial and longitudinal models |
title_full_unstemmed |
Robust estimation in familial and longitudinal models |
title_sort |
robust estimation in familial and longitudinal models |
publishDate |
2007 |
url |
http://collections.mun.ca/cdm/ref/collection/theses4/id/82127 |
genre |
Newfoundland studies University of Newfoundland |
genre_facet |
Newfoundland studies University of Newfoundland |
op_source |
Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries |
op_relation |
Electronic Theses and Dissertations (16.31 MB) -- http://collections.mun.ca/PDFs/theses/Bari_Wasimul2.pdf a2562080 http://collections.mun.ca/cdm/ref/collection/theses4/id/82127 |
op_rights |
The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. |
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