A Class of Univariate Non-Mesokurtic Distributions Using a Continuous Uniform Symmetrizer and Chi Generator

IF 1.6 Q1 STATISTICS & PROBABILITY
Kamala Naganathan Radhalakshmi, M. L. William
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引用次数: 0

Abstract

In a good number of real life situations, the observations on a random variable of interest tend to concentrate either too closely or too thinly around a central point but symmetrically like the normal distribution. The symmetric structure of the density function appears like that of a normal distribution but the concentration of the observations can be either thicker or thinner around the mean. This paper attempts to generate a family of densities that are symmetric like normal butwith different kurtosis. Drawing inspiration from a recent work on multivariate leptokurtic normal distribution, this paper seeks to consider the univariate case and adopt a different approach to generate a family to be called ’univariate non-mesokurtic normal’ family.The symmetricity of the densities is brought out by a uniform random variable while the kurtosis variation is brought about by a chi generator. Some of the properties of the resulting class of distributions and the pameter estimation are discussed.
一类使用连续均匀对称器和Chi发生器的单变量非中丘分布
在许多实际情况下,对感兴趣的随机变量的观察倾向于过于紧密或过于分散地集中在一个中心点周围,但像正态分布一样对称。密度函数的对称结构看起来像正态分布,但在平均值附近,观测值的浓度可能更厚,也可能更薄。本文试图生成一组像正态一样对称但峰度不同的密度。从最近关于多变量细峰正态分布的研究中获得灵感,本文试图考虑单变量情况,并采用不同的方法来生成一个称为“单变量非中丘正态”族。密度的对称性由均匀随机变量引起,峰度变化由chi发生器引起。讨论了所得到的一类分布的一些性质和参数估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Statistica
Statistica STATISTICS & PROBABILITY-
CiteScore
1.70
自引率
0.00%
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0
审稿时长
10 weeks
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