Variable selection in logistic regression, with special application to medical data

 

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dc.contributor.advisor Zucchini, Walter en_ZA
dc.contributor.author Joubert, Georgina en_ZA
dc.date.accessioned 2016-02-15T07:07:51Z
dc.date.available 2016-02-15T07:07:51Z
dc.date.issued 1994 en_ZA
dc.identifier.citation Joubert, G. 1994. Variable selection in logistic regression, with special application to medical data. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/17006
dc.description Bibliography: pages 121-126. en_ZA
dc.description.abstract In this thesis, the various methods of variable selection which have been proposed in the statistical, epidemiological and medical literature for prediction and estimation problems in logistic regression will be described. The procedures will be applied to medical data sets. On the basis of the literature review as well as the applications to examples, strengths and weaknesses of the approaches will be identified. The procedures will be compared on the basis of the results obtained, their appropriateness for the specific aim of the analysis, and demands they place on the analyst and researcher, intellectually and computationally. In particular, certain selection procedures using bootstrap samples, which have not been used before, will be investigated, and the partial Gauss discrepancy will be extended to the case of logistic regression. Recommendations will be made as to which approaches are the most suitable or most practical in different situations. Most statistical texts deal with issues regarding prediction, whereas the epidemiological literature focuses on estimation. It is therefore hoped that the thesis will be a useful reference for those, statistically or epidemiologically trained, who have to deal with issues regarding variable selection in logistic regression. When fitting models in general, and logistic regression models in particular, it is standard practice to determine the goodness of fit of models, and to ascertain whether outliers or influential observations are present in a data set. These aspects will not be discussed in this thesis, although they were considered when fitting the models. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Mathematical Statistics en_ZA
dc.title Variable selection in logistic regression, with special application to medical data en_ZA
dc.type Master Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Science en_ZA
dc.publisher.department Department of Statistical Sciences en_ZA
dc.type.qualificationlevel Masters
dc.type.qualificationname MSc en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Joubert, G. (1994). <i>Variable selection in logistic regression, with special application to medical data</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/17006 en_ZA
dc.identifier.chicagocitation Joubert, Georgina. <i>"Variable selection in logistic regression, with special application to medical data."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 1994. http://hdl.handle.net/11427/17006 en_ZA
dc.identifier.vancouvercitation Joubert G. Variable selection in logistic regression, with special application to medical data. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 1994 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/17006 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Joubert, Georgina AB - In this thesis, the various methods of variable selection which have been proposed in the statistical, epidemiological and medical literature for prediction and estimation problems in logistic regression will be described. The procedures will be applied to medical data sets. On the basis of the literature review as well as the applications to examples, strengths and weaknesses of the approaches will be identified. The procedures will be compared on the basis of the results obtained, their appropriateness for the specific aim of the analysis, and demands they place on the analyst and researcher, intellectually and computationally. In particular, certain selection procedures using bootstrap samples, which have not been used before, will be investigated, and the partial Gauss discrepancy will be extended to the case of logistic regression. Recommendations will be made as to which approaches are the most suitable or most practical in different situations. Most statistical texts deal with issues regarding prediction, whereas the epidemiological literature focuses on estimation. It is therefore hoped that the thesis will be a useful reference for those, statistically or epidemiologically trained, who have to deal with issues regarding variable selection in logistic regression. When fitting models in general, and logistic regression models in particular, it is standard practice to determine the goodness of fit of models, and to ascertain whether outliers or influential observations are present in a data set. These aspects will not be discussed in this thesis, although they were considered when fitting the models. DA - 1994 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 1994 T1 - Variable selection in logistic regression, with special application to medical data TI - Variable selection in logistic regression, with special application to medical data UR - http://hdl.handle.net/11427/17006 ER - en_ZA


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