Generalized hermite distribution modelling with the R package hermite

Abstract The Generalized Hermite distribution (and the Hermite distribution as a particular case) is often used for fitting count data in the presence of overdispersion or multimodality. Despite this, to our knowledge, no standard software packages have implemented specific functions to compute basi...

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Autores: Moriña, David, Higueras, Manuel, Puig, Pedro, Oliveira, María
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/59570
Acceso en línea:http://hdl.handle.net/10230/59570
http://dx.doi.org/10.32614/RJ-2015-035
Access Level:acceso abierto
Palabra clave:Programari
Probabilitats
Distribució (Teoria de la probabilitat)
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spelling Generalized hermite distribution modelling with the R package hermiteMoriña, DavidHigueras, ManuelPuig, PedroOliveira, MaríaProgramariProbabilitatsDistribució (Teoria de la probabilitat)Abstract The Generalized Hermite distribution (and the Hermite distribution as a particular case) is often used for fitting count data in the presence of overdispersion or multimodality. Despite this, to our knowledge, no standard software packages have implemented specific functions to compute basic probabilities and make simple statistical inference based on these distributions. We present here a set of computational tools that allows the user to face these difficulties by modelling with the Generalized Hermite distribution using the R package hermite. The package can also be used to generate random deviates from a Generalized Hermite distribution and to use basic functions to compute probabilities (density, cumulative density and quantile functions are available), to estimate parameters using the maximum likelihood method and to perform the likelihood ratio test for Poisson assumption against a Generalized Hermite alternative. In order to improve the density and quantile functions performance when the parameters are large, Edgeworth and Cornish-Fisher expansions have been used. Hermite regression is also a useful tool for modeling inflated count data, so its inclusion to a commonly used software like R will make this tool available to a wide range of potential users. Some examples of usage in several fields of application are also given.This work was partially funded by the grant MTM2012-31118, by the grant UNAB10-4E-378 co-funded by FEDER “A way to build Europe” and by the grant MTM2013-41383P from the Spanish Ministry of Economy and Competitiveness co-funded by the European Regional Development Fund (EDRF). We would like to thank professor David Giles for kindly providing some of the data sets used as examples in this paper.The R Foundation202420242015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/59570http://dx.doi.org/10.32614/RJ-2015-035reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésThe R Journal. 2015;7(2):263-74info:eu-repo/grantAgreement/ES/3PN/MTM2012-31118info:eu-repo/grantAgreement/ES/3PN/MTM2013-41383PThis article is licensed under a Creative Commons Attribution 3.0 Unported license.http://creativecommons.org/licenses/by/3.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/595702026-06-12T07:21:37Z
dc.title.none.fl_str_mv Generalized hermite distribution modelling with the R package hermite
title Generalized hermite distribution modelling with the R package hermite
spellingShingle Generalized hermite distribution modelling with the R package hermite
Moriña, David
Programari
Probabilitats
Distribució (Teoria de la probabilitat)
title_short Generalized hermite distribution modelling with the R package hermite
title_full Generalized hermite distribution modelling with the R package hermite
title_fullStr Generalized hermite distribution modelling with the R package hermite
title_full_unstemmed Generalized hermite distribution modelling with the R package hermite
title_sort Generalized hermite distribution modelling with the R package hermite
dc.creator.none.fl_str_mv Moriña, David
Higueras, Manuel
Puig, Pedro
Oliveira, María
author Moriña, David
author_facet Moriña, David
Higueras, Manuel
Puig, Pedro
Oliveira, María
author_role author
author2 Higueras, Manuel
Puig, Pedro
Oliveira, María
author2_role author
author
author
dc.subject.none.fl_str_mv Programari
Probabilitats
Distribució (Teoria de la probabilitat)
topic Programari
Probabilitats
Distribució (Teoria de la probabilitat)
description Abstract The Generalized Hermite distribution (and the Hermite distribution as a particular case) is often used for fitting count data in the presence of overdispersion or multimodality. Despite this, to our knowledge, no standard software packages have implemented specific functions to compute basic probabilities and make simple statistical inference based on these distributions. We present here a set of computational tools that allows the user to face these difficulties by modelling with the Generalized Hermite distribution using the R package hermite. The package can also be used to generate random deviates from a Generalized Hermite distribution and to use basic functions to compute probabilities (density, cumulative density and quantile functions are available), to estimate parameters using the maximum likelihood method and to perform the likelihood ratio test for Poisson assumption against a Generalized Hermite alternative. In order to improve the density and quantile functions performance when the parameters are large, Edgeworth and Cornish-Fisher expansions have been used. Hermite regression is also a useful tool for modeling inflated count data, so its inclusion to a commonly used software like R will make this tool available to a wide range of potential users. Some examples of usage in several fields of application are also given.
publishDate 2015
dc.date.none.fl_str_mv 2015
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/59570
http://dx.doi.org/10.32614/RJ-2015-035
url http://hdl.handle.net/10230/59570
http://dx.doi.org/10.32614/RJ-2015-035
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv The R Journal. 2015;7(2):263-74
info:eu-repo/grantAgreement/ES/3PN/MTM2012-31118
info:eu-repo/grantAgreement/ES/3PN/MTM2013-41383P
dc.rights.none.fl_str_mv This article is licensed under a Creative Commons Attribution 3.0 Unported license.
http://creativecommons.org/licenses/by/3.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv This article is licensed under a Creative Commons Attribution 3.0 Unported license.
http://creativecommons.org/licenses/by/3.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv The R Foundation
publisher.none.fl_str_mv The R Foundation
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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