Bekker, Andriette, 1958-Ferreira, Johannes TheodorusArashi, MohammadRowland, B.W.2019-01-152020A. 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: 10.1080/03610918.2018.1530785.0361-0918 (print)1532-4141 (online)10.1080/03610918.2018.1530785http://hdl.handle.net/2263/68149Some 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© 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.Approximating binomial distributionSkew-normalStochastic representationComputational methodsSymmetric distributionsStatistical characteristicsGeneralized normal distributionBinomial distributionTransport propertiesStochastic systemsNormal distributionSkew generalized normal (SGN)Postprint Article