A New Bivariate Distribution Obtained by Compounding the Bivariate Normal and Geometric Distributions

A New Bivariate Distribution Obtained by Compounding the Bivariate Normal and Geometric Distributions

Recently, Mahmoudi and Mahmoodian [7] introduced a new class of distributions which contains univariate normal–geometric distribution as a special case. This class of distributions are very flexible and can be used quite effectively to analyze skewed data. In this paper we propose a new bivariate di...

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Bibliographic Details
Main Authors: Eisa Mahmoudi, Hamed Mahmoodian
Format: Article
Language:English
Published: Atlantis Press 2017-05-01
Series:Journal of Statistical Theory and Applications (JSTA)
Subjects:
Normal distribution; Geometric distribution; EM algorithm; Maximum likelihood estimation.
Online Access:https://www.atlantis-press.com/article/25878525.pdf
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spelling doaj-0fcaf6c0a395458a913d1c958fe6eeb82020-11-25T02:28:18ZengAtlantis PressJournal of Statistical Theory and Applications (JSTA)1538-78872017-05-0116210.2991/jsta.2017.16.2.5A New Bivariate Distribution Obtained by Compounding the Bivariate Normal and Geometric DistributionsEisa MahmoudiHamed MahmoodianRecently, Mahmoudi and Mahmoodian [7] introduced a new class of distributions which contains univariate normal–geometric distribution as a special case. This class of distributions are very flexible and can be used quite effectively to analyze skewed data. In this paper we propose a new bivariate distribution with the normal–geometric distribution marginals. Different properties of this new bivariate distribution have been studied. This distribution has five unknown parameters. The EM algorithm is used to determine the maximum likelihood estimates of the parameters. We analyze one series of real data set for illustrative purposes.https://www.atlantis-press.com/article/25878525.pdfNormal distribution; Geometric distribution; EM algorithm; Maximum likelihood estimation.
collection DOAJ
language English
format Article
sources DOAJ
author Eisa Mahmoudi
Hamed Mahmoodian
spellingShingle Eisa Mahmoudi
Hamed Mahmoodian
A New Bivariate Distribution Obtained by Compounding the Bivariate Normal and Geometric Distributions
Journal of Statistical Theory and Applications (JSTA)
Normal distribution; Geometric distribution; EM algorithm; Maximum likelihood estimation.
author_facet Eisa Mahmoudi
Hamed Mahmoodian
author_sort Eisa Mahmoudi
title A New Bivariate Distribution Obtained by Compounding the Bivariate Normal and Geometric Distributions
title_short A New Bivariate Distribution Obtained by Compounding the Bivariate Normal and Geometric Distributions
title_full A New Bivariate Distribution Obtained by Compounding the Bivariate Normal and Geometric Distributions
title_fullStr A New Bivariate Distribution Obtained by Compounding the Bivariate Normal and Geometric Distributions
title_full_unstemmed A New Bivariate Distribution Obtained by Compounding the Bivariate Normal and Geometric Distributions
title_sort new bivariate distribution obtained by compounding the bivariate normal and geometric distributions
publisher Atlantis Press
series Journal of Statistical Theory and Applications (JSTA)
issn 1538-7887
publishDate 2017-05-01
description Recently, Mahmoudi and Mahmoodian [7] introduced a new class of distributions which contains univariate normal–geometric distribution as a special case. This class of distributions are very flexible and can be used quite effectively to analyze skewed data. In this paper we propose a new bivariate distribution with the normal–geometric distribution marginals. Different properties of this new bivariate distribution have been studied. This distribution has five unknown parameters. The EM algorithm is used to determine the maximum likelihood estimates of the parameters. We analyze one series of real data set for illustrative purposes.
topic Normal distribution; Geometric distribution; EM algorithm; Maximum likelihood estimation.
url https://www.atlantis-press.com/article/25878525.pdf
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