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dc.contributor.author | Bekker, Andriette, 1958-![]() |
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dc.contributor.author | Ferreira, Johannes Theodorus![]() |
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dc.contributor.author | Arashi, Mohammad![]() |
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dc.contributor.author | Rowland, B.W.![]() |
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dc.date.accessioned | 2019-01-15T11:23:10Z | |
dc.date.issued | 2020 | |
dc.description.abstract | Some characteristics of the normal distribution may not be ideal to model in many applications. We develop a skew generalized normal (SGN) distribution by applying a skewing method to a generalized normal distribution, and study some meaningful statistical characteristics. Computational methods to approximate, and a well-constructed efficient computational approach to estimate these characteristics, are presented. A stochastic representation of this distribution is derived and numerically implemented. The skewing method is extended to the elliptical class resulting in a more general framework for skewing symmetric distributions. The proposed distribution is applied in a fitting context and to approximate particular binomial distributions. | en_ZA |
dc.description.department | Statistics | en_ZA |
dc.description.embargo | 2019-12-31 | |
dc.description.librarian | hj2019 | en_ZA |
dc.description.sponsorship | The National Research Foundation, South Africa (Grant number: 91497; 109214; 102640). | en_ZA |
dc.description.uri | http://www.tandfonline.com/loi/lssp20 | en_ZA |
dc.identifier.citation | A. Bekker, J. T. Ferreira, M. Arashi & B. W. Rowland (2020): Computational methods applied to a skewed generalized normal family, Communications in Statistics - Simulation and Computation 49(11): 2930-2943, DOI: 10.1080/03610918.2018.1530785. | en_ZA |
dc.identifier.issn | 0361-0918 (print) | |
dc.identifier.issn | 1532-4141 (online) | |
dc.identifier.other | 10.1080/03610918.2018.1530785 | |
dc.identifier.uri | http://hdl.handle.net/2263/68149 | |
dc.language.iso | en | en_ZA |
dc.publisher | Taylor and Francis | en_ZA |
dc.rights | © 2018 Taylor & Francis Group, LLC. This is an electronic version of an article published in Communications in Statistics : Simulation and Computation, vol. 49, no. 11, pp. 2930-2943, 2020. doi : 10.1080/03610918.2018.1530785. Communications in Statistics : Simulation and Computation is available online at : http://www.tandfonline.comloi/lssp20. | en_ZA |
dc.subject | Approximating binomial distribution | en_ZA |
dc.subject | Skew-normal | en_ZA |
dc.subject | Stochastic representation | en_ZA |
dc.subject | Computational methods | en_ZA |
dc.subject | Symmetric distributions | en_ZA |
dc.subject | Statistical characteristics | en_ZA |
dc.subject | Generalized normal distribution | en_ZA |
dc.subject | Binomial distribution | en_ZA |
dc.subject | Transport properties | en_ZA |
dc.subject | Stochastic systems | en_ZA |
dc.subject | Normal distribution | en_ZA |
dc.subject | Skew generalized normal (SGN) | en_ZA |
dc.title | Computational methods applied to a skewed generalized normal family | en_ZA |
dc.type | Postprint Article | en_ZA |