关于广义非对称正态分布的若干问题

IF 1.6 Q1 STATISTICS & PROBABILITY Statistica Pub Date : 2018-01-11 DOI:10.6092/ISSN.1973-2201/7134
C. Kumar, G. V. Anila
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引用次数: 0

摘要

正态分布和偏斜正态分布不足以对多模态数据情况进行建模。为了克服正态分布和偏斜正态分布的缺点,Kumar和Anusree(2011)提出了一类新的分布,即“标准正态分布与偏斜正态分配的广义混合(GMNSND)”。在本文中,我们将GMNSND的扩展版本视为一类广泛的多模态不对称正态分布,并研究其一些重要的分布性质。还定义了该模型的位置尺度扩展,并讨论了用最大似然法对其参数的估计。此外,考虑了四个真实生活数据集来说明该模型的有用性。
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On Some Aspects of a Generalized Asymmetric Normal Distribution
The normal and skew normal distributions are not adequate enough for modeling plurimodal data situations. In order to overcome this drawback of normal and skew normal distribution, Kumar and Anusree (2011) proposed a new class of distribution namely "the generalized mixture of standard normal and skew normal distributions (GMNSND)". In this paper we consider an extended version of the GMNSND as a wide class of plurimodal asymmetric normal distribution and investigate some of its important distributional properties. Location-scale extension of the proposed model is also defined and discussed the estimation of its parameters by method of maximum likelihood. Further, four real life data sets are considered for illustrating the usefulness of this model.
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来源期刊
Statistica
Statistica STATISTICS & PROBABILITY-
CiteScore
1.70
自引率
0.00%
发文量
0
审稿时长
10 weeks
期刊最新文献
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