A Characterization of the Compound Multiparameter Hermite Gamma Distribution via Gauss's Principle

We consider the class of those distributions that satisfy Gauss's principle (the maximum likelihood estimator of the mean is the sample mean) and have a parameter orthogonal to the mean. It is shown that this so-called “mean orthogonal class” is closed under convolution. A previous characteriza...

Descripción completa

Detalles Bibliográficos
Autor principal: Hürlimann, Werner
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3835847/
https://www.ncbi.nlm.nih.gov/pubmed/24298220
http://dx.doi.org/10.1155/2013/468418
_version_ 1782292218293256192
author Hürlimann, Werner
author_facet Hürlimann, Werner
author_sort Hürlimann, Werner
collection PubMed
description We consider the class of those distributions that satisfy Gauss's principle (the maximum likelihood estimator of the mean is the sample mean) and have a parameter orthogonal to the mean. It is shown that this so-called “mean orthogonal class” is closed under convolution. A previous characterization of the compound gamma characterization of random sums is revisited and clarified. A new characterization of the compound distribution with multiparameter Hermite count distribution and gamma severity distribution is obtained.
format Online
Article
Text
id pubmed-3835847
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher Hindawi Publishing Corporation
record_format MEDLINE/PubMed
spelling pubmed-38358472013-12-02 A Characterization of the Compound Multiparameter Hermite Gamma Distribution via Gauss's Principle Hürlimann, Werner ScientificWorldJournal Research Article We consider the class of those distributions that satisfy Gauss's principle (the maximum likelihood estimator of the mean is the sample mean) and have a parameter orthogonal to the mean. It is shown that this so-called “mean orthogonal class” is closed under convolution. A previous characterization of the compound gamma characterization of random sums is revisited and clarified. A new characterization of the compound distribution with multiparameter Hermite count distribution and gamma severity distribution is obtained. Hindawi Publishing Corporation 2013-10-31 /pmc/articles/PMC3835847/ /pubmed/24298220 http://dx.doi.org/10.1155/2013/468418 Text en Copyright © 2013 Werner Hürlimann. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Hürlimann, Werner
A Characterization of the Compound Multiparameter Hermite Gamma Distribution via Gauss's Principle
title A Characterization of the Compound Multiparameter Hermite Gamma Distribution via Gauss's Principle
title_full A Characterization of the Compound Multiparameter Hermite Gamma Distribution via Gauss's Principle
title_fullStr A Characterization of the Compound Multiparameter Hermite Gamma Distribution via Gauss's Principle
title_full_unstemmed A Characterization of the Compound Multiparameter Hermite Gamma Distribution via Gauss's Principle
title_short A Characterization of the Compound Multiparameter Hermite Gamma Distribution via Gauss's Principle
title_sort characterization of the compound multiparameter hermite gamma distribution via gauss's principle
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3835847/
https://www.ncbi.nlm.nih.gov/pubmed/24298220
http://dx.doi.org/10.1155/2013/468418
work_keys_str_mv AT hurlimannwerner acharacterizationofthecompoundmultiparameterhermitegammadistributionviagausssprinciple
AT hurlimannwerner characterizationofthecompoundmultiparameterhermitegammadistributionviagausssprinciple

Ejemplares similares