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Asymmetric Bimodal Exponential Power Distribution on the Real Line

The asymmetric bimodal exponential power (ABEP) distribution is an extension of the generalized gamma distribution to the real line via adding two parameters that fit the shape of peakedness in bimodality on the real line. The special values of peakedness parameters of the distribution are a combina...

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Autor principal: Çankaya, Mehmet Niyazi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512200/
https://www.ncbi.nlm.nih.gov/pubmed/33265113
http://dx.doi.org/10.3390/e20010023
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author Çankaya, Mehmet Niyazi
author_facet Çankaya, Mehmet Niyazi
author_sort Çankaya, Mehmet Niyazi
collection PubMed
description The asymmetric bimodal exponential power (ABEP) distribution is an extension of the generalized gamma distribution to the real line via adding two parameters that fit the shape of peakedness in bimodality on the real line. The special values of peakedness parameters of the distribution are a combination of half Laplace and half normal distributions on the real line. The distribution has two parameters fitting the height of bimodality, so capacity of bimodality is enhanced by using these parameters. Adding a skewness parameter is considered to model asymmetry in data. The location-scale form of this distribution is proposed. The Fisher information matrix of these parameters in ABEP is obtained explicitly. Properties of ABEP are examined. Real data examples are given to illustrate the modelling capacity of ABEP. The replicated artificial data from maximum likelihood estimates of parameters of ABEP and other distributions having an algorithm for artificial data generation procedure are provided to test the similarity with real data. A brief simulation study is presented.
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spelling pubmed-75122002020-11-09 Asymmetric Bimodal Exponential Power Distribution on the Real Line Çankaya, Mehmet Niyazi Entropy (Basel) Article The asymmetric bimodal exponential power (ABEP) distribution is an extension of the generalized gamma distribution to the real line via adding two parameters that fit the shape of peakedness in bimodality on the real line. The special values of peakedness parameters of the distribution are a combination of half Laplace and half normal distributions on the real line. The distribution has two parameters fitting the height of bimodality, so capacity of bimodality is enhanced by using these parameters. Adding a skewness parameter is considered to model asymmetry in data. The location-scale form of this distribution is proposed. The Fisher information matrix of these parameters in ABEP is obtained explicitly. Properties of ABEP are examined. Real data examples are given to illustrate the modelling capacity of ABEP. The replicated artificial data from maximum likelihood estimates of parameters of ABEP and other distributions having an algorithm for artificial data generation procedure are provided to test the similarity with real data. A brief simulation study is presented. MDPI 2018-01-03 /pmc/articles/PMC7512200/ /pubmed/33265113 http://dx.doi.org/10.3390/e20010023 Text en © 2018 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Çankaya, Mehmet Niyazi
Asymmetric Bimodal Exponential Power Distribution on the Real Line
title Asymmetric Bimodal Exponential Power Distribution on the Real Line
title_full Asymmetric Bimodal Exponential Power Distribution on the Real Line
title_fullStr Asymmetric Bimodal Exponential Power Distribution on the Real Line
title_full_unstemmed Asymmetric Bimodal Exponential Power Distribution on the Real Line
title_short Asymmetric Bimodal Exponential Power Distribution on the Real Line
title_sort asymmetric bimodal exponential power distribution on the real line
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512200/
https://www.ncbi.nlm.nih.gov/pubmed/33265113
http://dx.doi.org/10.3390/e20010023
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