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Bayesian kernel density estimation
Dissertation (MSc)--University of Pretoria, 2017.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2018
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| _version_ | 1867613599661293568 |
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| access_status_str | Open Access |
| author2 | De Waal, Alta |
| author_browse | De Waal, Alta |
| author_facet | De Waal, Alta |
| collection | Thesis |
| dc_rights_str_mv | © 2018 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc)--University of Pretoria, 2017. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/64692 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:38:42.927Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/64692 Bayesian kernel density estimation De Waal, Alta u120148942tuks.co.za Van der Walt, Christiaan M. Rademeyer, Estian Kernel density estimation Bayes Credit scoring Machine learning UCTD Dissertation (MSc)--University of Pretoria, 2017. This dissertation investigates the performance of two-class classi cation credit scoring data sets with low default ratios. The standard two-class parametric Gaussian and naive Bayes (NB), as well as the non-parametric Parzen classi ers are extended, using Bayes' rule, to include either a class imbalance or a Bernoulli prior. This is done with the aim of addressing the low default probability problem. Furthermore, the performance of Parzen classi cation with Silverman and Minimum Leave-one-out Entropy (MLE) Gaussian kernel bandwidth estimation is also investigated. It is shown that the non-parametric Parzen classi ers yield superior classi cation power. However, there is a longing for these non-parametric classi ers to posses a predictive power, such as exhibited by the odds ratio found in logistic regression (LR). The dissertation therefore dedicates a section to, amongst other things, study the paper entitled \Model-Free Objective Bayesian Prediction" (Bernardo 1999). Since this approach to Bayesian kernel density estimation is only developed for the univariate and the uncorrelated multivariate case, the section develops a theoretical multivariate approach to Bayesian kernel density estimation. This approach is theoretically capable of handling both correlated as well as uncorrelated features in data. This is done through the assumption of a multivariate Gaussian kernel function and the use of an inverse Wishart prior. The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the NRF. Statistics MSc Unrestricted 2018-04-23T09:11:06Z 2018-04-23T09:11:06Z 2018-04-13 2017 Dissertation Rademeyer, E 2017, Bayesian kernel density estimation, MSc Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/64692> A2018 http://hdl.handle.net/2263/64692 en © 2018 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Kernel density estimation Bayes Credit scoring Machine learning UCTD Bayesian kernel density estimation |
| title | Bayesian kernel density estimation |
| title_full | Bayesian kernel density estimation |
| title_fullStr | Bayesian kernel density estimation |
| title_full_unstemmed | Bayesian kernel density estimation |
| title_short | Bayesian kernel density estimation |
| title_sort | bayesian kernel density estimation |
| topic | Kernel density estimation Bayes Credit scoring Machine learning UCTD |
| url | http://hdl.handle.net/2263/64692 |