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DC Field | Value | Language |
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dc.date.accessioned | 2021-11-09T10:58:47Z | - |
dc.date.available | 2021-11-09T10:58:47Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Lentini, G. (2021). Multivariate kernel discrimination for bank loans (Bachelor's dissertation). | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/83580 | - |
dc.description | B.Sc. (Hons)(Melit.) | en_GB |
dc.description.abstract | The purpose of this dissertation is to obtain a kernel discriminant model to classify bank loans and determine which loans are at risk of default. This study starts by introducing the concept of kernel density estimation, which is a widely used non-parametric technique to obtain an estimate for the probability density function. This procedure is based on two main parameters: the kernel function and the bandwidth, the latter being the crucial parameter. The main focus of this dissertation is the multivariate case of kernel density estimation and this nonparametric technique is later applied to discriminant analysis to obtain kernel discrimination. This is a method which classifies observations into a predetermined number of distinct and disjoint classes. Finally, we apply multivariate kernel discriminant analysis to a sample of bank loans in order to determine which loans can be actually classified as defaulted. | en_GB |
dc.language.iso | en | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Banks and banking -- Malta | en_GB |
dc.subject | Bank loans -- Malta | en_GB |
dc.subject | Kernel functions | en_GB |
dc.subject | Multivariate analysis | en_GB |
dc.subject | Discriminant analysis | en_GB |
dc.title | Multivariate kernel discrimination for bank loans | en_GB |
dc.type | bachelorThesis | en_GB |
dc.rights.holder | The 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.institution | University of Malta | en_GB |
dc.publisher.department | Faculty of Science. Department of Statistics and Operations Research | en_GB |
dc.description.reviewed | N/A | en_GB |
dc.contributor.creator | Lentini, Gabriele (2021) | - |
Appears in Collections: | Dissertations - FacSci - 2021 Dissertations - FacSciSOR - 2021 |
Files in This Item:
File | Description | Size | Format | |
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21BSCMSOR009.pdf Restricted Access | 2.79 MB | Adobe PDF | View/Open Request a copy |
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