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Quantile-based generalized logistic distribution
Dissertation (MSc)--University of Pretoria, 2014.
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| Other Authors: | |
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2022
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| _version_ | 1867613652963557377 |
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| access_status_str | Open Access |
| author2 | Van Staden, Paul J. |
| author_browse | Van Staden, Paul J. |
| author_facet | Van Staden, Paul J. |
| collection | Thesis |
| dc_rights_str_mv | © 2021 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, 2014. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/83687 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:39:33.692Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| 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:2263/83687 Quantile-based generalized logistic distribution Van Staden, Paul J. paul.vanstaden@up.ac.za Omachar, Brenda V. Generalized logistic distribution L-Moments Quantile function UCTD Dissertation (MSc)--University of Pretoria, 2014. This dissertation proposes the development of a new quantile-based generalized logistic distribution GLDQB, by using the quantile function of the generalized logistic distribution (GLO) as the basic building block. This four-parameter distribution is highly flexible with respect to distributional shape in that it explains extensive levels of skewness and kurtosis through the inclusion of two shape parameters. The parameter space as well as the distributional shape properties are discussed at length. The distribution is characterized through its -moments and an estimation algorithm is presented for estimating the distribution’s parameters with method of -moments estimation. This new distribution is then used to fit and approximate the probability of a data set. Statistics MSc Unrestricted 2022-02-09T06:51:37Z 2022-02-09T06:51:37Z 2014 2014 Dissertation * http://hdl.handle.net/2263/83687 en © 2021 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 | Generalized logistic distribution L-Moments Quantile function UCTD Quantile-based generalized logistic distribution |
| title | Quantile-based generalized logistic distribution |
| title_full | Quantile-based generalized logistic distribution |
| title_fullStr | Quantile-based generalized logistic distribution |
| title_full_unstemmed | Quantile-based generalized logistic distribution |
| title_short | Quantile-based generalized logistic distribution |
| title_sort | quantile based generalized logistic distribution |
| topic | Generalized logistic distribution L-Moments Quantile function UCTD |
| url | http://hdl.handle.net/2263/83687 |