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Memorial University - Electronic Theses and Dissertations 5
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Document Description
TitleGeneralized quasilikelihood method for misclassified longitudinal binary data
AuthorTao, Yi, 1981-
DescriptionThesis (M.A.S.)--Memorial University of Newfoundland, 2010. Mathematics and Statistics
Date2010
Paginationvii, 70 leaves : ill.
SubjectBiometry; Linear models (Statistics); Logistic distribution; Regression analysis
DegreeM.A.S.
Degree GrantorMemorial University of Newfoundland. Dept. of Mathematics and Statistics
DisciplineMathematics and Statistics
LanguageEng
NotesBibliography: leaves 68-70.
AbstractIn this practicum we develop the generalized quasi-likelihood approach to analyzing longitudinal binary data with misclassification in response. We utilize the method of Monahan and Stefanski (1992) to approximate the expectation of an unknown function involved in the calculation of the means and covariances, which are further used to develop GQL estimating functions. The results of an intensive simulation study show that the proposed method works very well in all the preselected settings. The efficiency gain as compared to the naive method is remarkable. The method is robust in the sense that the performance varies just slightly when model parameters change in the simulation. -- Keywords: Logit link; longitudinal binary response; GQL; Misclassification.
TypeText
Resource TypeElectronic thesis or dissertation
FormatImage/jpeg; Application/pdf
SourcePaper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries
Local Identifiera3497997
RightsThe 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.
CollectionElectronic Theses and Dissertations
Scanning StatusCompleted
PDF File(3.14 MB) -- http://collections.mun.ca/PDFs/theses/Tao_Yi.pdf
CONTENTdm file name10762.cpd