Harris skewed normal distribution

In this paper a new family of Harris skewed normal distributions (HSND) is proposed. The probability density function of the HSND can be both positively and negatively skewed, unimodal and multimodal and can be symmetric with heavy tails. Monte Carlo simulations are conducted to evaluate the applica...

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Published in:Communications in statistics. Theory and methods 2023-09, Vol.52 (18), p.6597-6615
Main Authors: Al-Kandari, Noriah, Aly, Emad-Eldin A. A., Benkherouf, Lakdere
Format: Article
Language:English
Subjects:
EM algorithm
Harris distribution
infinite divisible
maximum likelihood estimators
moment generating function
Normal distribution
Probability density functions
random sums
Skewed distributions
Statistical analysis
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description In this paper a new family of Harris skewed normal distributions (HSND) is proposed. The probability density function of the HSND can be both positively and negatively skewed, unimodal and multimodal and can be symmetric with heavy tails. Monte Carlo simulations are conducted to evaluate the applicability of the proposed family of distributions. The HSND family is used to fit three different real data sets.
doi_str_mv 10.1080/03610926.2022.2032169
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subjects EM algorithm
Harris distribution
infinite divisible
maximum likelihood estimators
moment generating function
Normal distribution
Probability density functions
random sums
Skewed distributions
Statistical analysis
title Harris skewed normal distribution
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