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

Simple item page
dc.contributor.advisorZucchini, Walteren_ZA
dc.contributor.authorJoubert, Georginaen_ZA
dc.date.accessioned2016-02-15T07:07:51Z
dc.date.available2016-02-15T07:07:51Z
dc.date.issued1994en_ZA
dc.descriptionBibliography: pages 121-126.en_ZA
dc.description.abstractIn 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.identifier.apacitationJoubert, 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/11427/17006en_ZA
dc.identifier.chicagocitationJoubert, 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/11427/17006en_ZA
dc.identifier.citationJoubert, G. 1994. Variable selection in logistic regression, with special application to medical data. University of Cape Town.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
dc.identifier.urihttp://hdl.handle.net/11427/17006
dc.identifier.vancouvercitationJoubert 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/11427/17006en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Statistical Sciencesen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherMathematical Statisticsen_ZA
dc.titleVariable selection in logistic regression, with special application to medical dataen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
thesis_sci_1994_joubert_georgina.pdf
Size:
1.83 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Masters