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dc.identifier.citationSpiteri, L. (2017). Ridge logistic regression for classification: a comparison study (Bachelor's dissertation).en_GB
dc.description.abstractLogistic regression (LR) is one of the most widely used multivariate statistical techniques for modelling dichotomous response variables. Although LR was originally used to determine a parsimonious model which best describes the potential relationship that may exist between the response variable and the set of explanatory variables, nowadays it has been found to be useful for classification purposes. The maximum likelihood estimation procedure in LR is known to be negatively affected when the data is characterized by any of the following scenarios: (i) multicollinearity, (ii) high-dimensionality, where the number of explanatory variables p is greater than the sample size n, and (iii) complete or quasi-complete separation, giving rise to unreliable or undefined parameter estimates. The Ridge estimator can be used in LR as a possible solution to the aforementioned scenarios. One of the greatest challenges in Ridge logistic regression (RLR) is that of finding the optimal shrinkage parameter. The use of cross-validation techniques to evaluate the value of the shrinkage parameter are explored in some detail. The focus in this dissertation is on the use of RLR as a classification method, and thus it will be compared to a popular classification technique, namely linear discriminant analysis (LDA). Unfortunately, classical LDA also fails in scenarios (i) and (ii) mentioned above. One possible solution is to apply dimension reduction techniques, such as Partial Least Squares. The performance of the classification methods will be explored by applying them on four real-life datasets having different characteristics.en_GB
dc.subjectRegression analysisen_GB
dc.subjectRidge regression (Statistics)en_GB
dc.subjectLogistic distributionen_GB
dc.titleRidge logistic regression for classification : a comparison studyen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.publisher.institutionUniversity of Maltaen_GB
dc.publisher.departmentFaculty of Science. Department of Statistics and Operations Researchen_GB
dc.contributor.creatorSpiteri, Luke (2017)-
Appears in Collections:Dissertations - FacSci - 2017
Dissertations - FacSciSOR - 2017

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