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A flexible generalization of the skew normal distribution based on a weighted normal distribution

机译:基于加权正态分布的偏态正态分布的灵活概括

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The skew normal distribution of Azzalini (Scand J Stat 12:171-178, 1985) has been found suitable for unimodal density but with some skewness present. Through this article, we introduce a flexible extension of the Azzalini (Scand J Stat 12:171-178, 1985) skew normal distribution based on a symmetric component normal distribution (Gui et al. in J Stat Theory Appl 12(1):55-66, 2013). The proposed model can efficiently capture the bimodality, skewness and kurtosis criteria and heavy-tail property. The paper presents various basic properties of this family of distributions and provides two stochastic representations which are useful for obtaining theoretical properties and to simulate from the distribution. Further, maximum likelihood estimation of the parameters is studied numerically by simulation and the distribution is investigated by carrying out comparative fitting of three real datasets.
机译:已经发现Azzalini的偏态正态分布(Scand J Stat 12:171-178,1985)适合于单峰密度,但存在一定的偏态。通过本文,我们介绍了基于对称分量正态分布(Gui等人,J Stat Theory Appl 12(1):55)的Azzalini的灵活扩展(Scand J Stat 12:171-178,1985)。 -66,2013)。提出的模型可以有效地捕获双峰,偏度和峰度标准以及重尾特性。本文介绍了该分布族的各种基本性质,并提供了两种随机表示形式,可用于获得理论性质并从分布进行模拟。此外,通过模拟数值研究了参数的最大似然估计,并通过对三个真实数据集进行比较拟合研究了分布。

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