Simple models for macro-parasite distributions in hosts

Autores
López, Gonzalo Maximiliano; Aparicio, Juan Pablo
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Negative binomial distribution is the most used distribution to model macro-parasite burden in hosts. However reliable maximum likelihood parameter estimation from data is far from trivial. No closed formula is available and numerical estimation requires sophisticated methods. Using data from the literature we show that simple alternatives to negative binomial, like zero-inflated geometric or hurdle geometric distributions, produce a good and even better fit to data than negative binomial distribution. We derived closed simple formulas for the maximum likelihood parameter estimation which constitutes a significant advantage of these distributions over negative binomial distribution.
Fil: López, Gonzalo Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; Argentina
Fil: Aparicio, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; Argentina
Materia
Hurdle geometric distribution
Macroparasite
Maximum likelihood estimation
Zero-inflated geometric distribution
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/203554
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Simple models for macro-parasite distributions in hostsLópez, Gonzalo MaximilianoAparicio, Juan PabloHurdle geometric distributionMacroparasiteMaximum likelihood estimationZero-inflated geometric distributionhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Negative binomial distribution is the most used distribution to model macro-parasite burden in hosts. However reliable maximum likelihood parameter estimation from data is far from trivial. No closed formula is available and numerical estimation requires sophisticated methods. Using data from the literature we show that simple alternatives to negative binomial, like zero-inflated geometric or hurdle geometric distributions, produce a good and even better fit to data than negative binomial distribution. We derived closed simple formulas for the maximum likelihood parameter estimation which constitutes a significant advantage of these distributions over negative binomial distribution.Fil: López, Gonzalo Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; ArgentinaFil: Aparicio, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; ArgentinaCornell University2022-02-23info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/203554López, Gonzalo Maximiliano; Aparicio, Juan Pablo; Simple models for macro-parasite distributions in hosts; Cornell University; arXiv.org; 2022; 23-2-2022; 1-162331-8422CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://arxiv.org/abs/2202.11282info:eu-repo/semantics/altIdentifier/doi/10.48550/arXiv.2202.11282info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:23:32Zoai:ri.conicet.gov.ar:11336/203554instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:23:32.538CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Simple models for macro-parasite distributions in hosts
title Simple models for macro-parasite distributions in hosts
spellingShingle Simple models for macro-parasite distributions in hosts
López, Gonzalo Maximiliano
Hurdle geometric distribution
Macroparasite
Maximum likelihood estimation
Zero-inflated geometric distribution
title_short Simple models for macro-parasite distributions in hosts
title_full Simple models for macro-parasite distributions in hosts
title_fullStr Simple models for macro-parasite distributions in hosts
title_full_unstemmed Simple models for macro-parasite distributions in hosts
title_sort Simple models for macro-parasite distributions in hosts
dc.creator.none.fl_str_mv López, Gonzalo Maximiliano
Aparicio, Juan Pablo
author López, Gonzalo Maximiliano
author_facet López, Gonzalo Maximiliano
Aparicio, Juan Pablo
author_role author
author2 Aparicio, Juan Pablo
author2_role author
dc.subject.none.fl_str_mv Hurdle geometric distribution
Macroparasite
Maximum likelihood estimation
Zero-inflated geometric distribution
topic Hurdle geometric distribution
Macroparasite
Maximum likelihood estimation
Zero-inflated geometric distribution
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Negative binomial distribution is the most used distribution to model macro-parasite burden in hosts. However reliable maximum likelihood parameter estimation from data is far from trivial. No closed formula is available and numerical estimation requires sophisticated methods. Using data from the literature we show that simple alternatives to negative binomial, like zero-inflated geometric or hurdle geometric distributions, produce a good and even better fit to data than negative binomial distribution. We derived closed simple formulas for the maximum likelihood parameter estimation which constitutes a significant advantage of these distributions over negative binomial distribution.
Fil: López, Gonzalo Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; Argentina
Fil: Aparicio, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; Argentina
description Negative binomial distribution is the most used distribution to model macro-parasite burden in hosts. However reliable maximum likelihood parameter estimation from data is far from trivial. No closed formula is available and numerical estimation requires sophisticated methods. Using data from the literature we show that simple alternatives to negative binomial, like zero-inflated geometric or hurdle geometric distributions, produce a good and even better fit to data than negative binomial distribution. We derived closed simple formulas for the maximum likelihood parameter estimation which constitutes a significant advantage of these distributions over negative binomial distribution.
publishDate 2022
dc.date.none.fl_str_mv 2022-02-23
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/203554
López, Gonzalo Maximiliano; Aparicio, Juan Pablo; Simple models for macro-parasite distributions in hosts; Cornell University; arXiv.org; 2022; 23-2-2022; 1-16
2331-8422
CONICET Digital
CONICET
url http://hdl.handle.net/11336/203554
identifier_str_mv López, Gonzalo Maximiliano; Aparicio, Juan Pablo; Simple models for macro-parasite distributions in hosts; Cornell University; arXiv.org; 2022; 23-2-2022; 1-16
2331-8422
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://arxiv.org/abs/2202.11282
info:eu-repo/semantics/altIdentifier/doi/10.48550/arXiv.2202.11282
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Cornell University
publisher.none.fl_str_mv Cornell University
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846083382438526976
score 13.221938

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