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Skew Laplace candidates emanating from scale mixtures for insightful computational modelling
Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2022.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2023
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| _version_ | 1867613523068059648 |
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| access_status_str | Open Access |
| author2 | Bekker, Andriette, 1958- |
| author_browse | Bekker, Andriette, 1958- |
| author_facet | Bekker, Andriette, 1958- |
| collection | Thesis |
| dc_rights_str_mv | © 2022 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 | Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/89030 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:29.889Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| 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:2263/89030 Skew Laplace candidates emanating from scale mixtures for insightful computational modelling Bekker, Andriette, 1958- arnoldusotto@gmail.com Ferreira, Johan T. Otto, Arnoldus F. Laplace Finite mixtures Scale mixtures Double exponential EM algorithm UCTD Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2022. The search for appropriate and flexible models for describing complex data sets, often with departure from normality, remains a main interest in various computational research fields. In this study, the focus is on developing flexible skew Laplace scale mixture distributions to model these data sets. Each member of the collection of distributions is obtained by dividing the scale parameter of a conditional skew Laplace distribution by a purposefully chosen mixing random variable. Highly-peaked, heavy tailed skew models with relevance and impact in different fields are achieved. Finite mixtures consisting of the members of the skew Laplace scale mixture models are developed, further extending the flexibility of the distributions by being able to potentially account for multimodality. The maximum likelihood estimates of the parameters for all the members of the developed models are obtained via an EM algorithm. The models are fit to bodily injury claims of Massachusetts to show the applicability and compared to other existing flexible distributions. Various goodness of fit measures are used to validate the performance of the models as valid alternatives. NRF Statistics MSc (Advanced Data Analytics) Unrestricted 2023-01-30T14:24:06Z 2023-01-30T14:24:06Z 2023 2023 Mini Dissertation * A2023 https://repository.up.ac.za/handle/2263/89030 https://doi.org/10.1080/10920277.2005.10596206 en © 2022 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 | Laplace Finite mixtures Scale mixtures Double exponential EM algorithm UCTD Skew Laplace candidates emanating from scale mixtures for insightful computational modelling |
| title | Skew Laplace candidates emanating from scale mixtures for insightful computational modelling |
| title_full | Skew Laplace candidates emanating from scale mixtures for insightful computational modelling |
| title_fullStr | Skew Laplace candidates emanating from scale mixtures for insightful computational modelling |
| title_full_unstemmed | Skew Laplace candidates emanating from scale mixtures for insightful computational modelling |
| title_short | Skew Laplace candidates emanating from scale mixtures for insightful computational modelling |
| title_sort | skew laplace candidates emanating from scale mixtures for insightful computational modelling |
| topic | Laplace Finite mixtures Scale mixtures Double exponential EM algorithm UCTD |
| url | https://repository.up.ac.za/handle/2263/89030 https://doi.org/10.1080/10920277.2005.10596206 |