Goodness-of-fit test for directional data

Autores
Boente Boente, Graciela Lina; González Manteiga, Wenceslao; Rodriguez, Daniela Andrea
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this paper, we study the problem of testing the hypothesis on whether the density f of a random variable on a sphere belongs to a given parametric class of densities. We propose two test statistics based on the L2 and L1 distances between a non-parametric density estimator adapted to circular data and a smoothed version of the specified density. The asymptotic distribution of the L2 test statistic is provided under the null hypothesis and contiguous alternatives. We also consider a bootstrap method to approximate the distribution of both test statistics. Through a simulation study, we explore the moderate sample performance of the proposed tests under the null hypothesis and under different alternatives. Finally, the procedure is illustrated by analysing a real data set based on wind direction measurements.
Fil: Boente Boente, Graciela Lina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas ; Argentina
Fil: González Manteiga, Wenceslao. Universidad de Santiago de Compostela; España
Fil: Rodriguez, Daniela Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas ; Argentina
Materia
Asymptotic Properties
Bootstrap Tests
Density Estimation
Hypothesis Testing
Maximum Likelihood Estimators
Spherical Data
Von Mises 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:11336/37348
Acceder
id CONICETDig_70541338d2e0d63397267d6afc207a32
oai_identifier_str oai:ri.conicet.gov.ar:11336/37348
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Goodness-of-fit test for directional dataBoente Boente, Graciela LinaGonzález Manteiga, WenceslaoRodriguez, Daniela AndreaAsymptotic PropertiesBootstrap TestsDensity EstimationHypothesis TestingMaximum Likelihood EstimatorsSpherical DataVon Mises Distributionhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1In this paper, we study the problem of testing the hypothesis on whether the density f of a random variable on a sphere belongs to a given parametric class of densities. We propose two test statistics based on the L2 and L1 distances between a non-parametric density estimator adapted to circular data and a smoothed version of the specified density. The asymptotic distribution of the L2 test statistic is provided under the null hypothesis and contiguous alternatives. We also consider a bootstrap method to approximate the distribution of both test statistics. Through a simulation study, we explore the moderate sample performance of the proposed tests under the null hypothesis and under different alternatives. Finally, the procedure is illustrated by analysing a real data set based on wind direction measurements.Fil: Boente Boente, Graciela Lina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas ; ArgentinaFil: González Manteiga, Wenceslao. Universidad de Santiago de Compostela; EspañaFil: Rodriguez, Daniela Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas ; ArgentinaWiley Blackwell Publishing, Inc2014-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/37348Boente Boente, Graciela Lina; González Manteiga, Wenceslao; Rodriguez, Daniela Andrea; Goodness-of-fit test for directional data; Wiley Blackwell Publishing, Inc; Scandinavian Journal Of Statistics; 41; 1; 3-2014; 259-2750303-6898CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1111/sjos.12020/abstractinfo:eu-repo/semantics/altIdentifier/doi/10.1111/sjos.12020info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:36:36Zoai:ri.conicet.gov.ar:11336/37348instacron: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:34982025-09-29 09:36:36.546CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Goodness-of-fit test for directional data
title Goodness-of-fit test for directional data
spellingShingle Goodness-of-fit test for directional data
Boente Boente, Graciela Lina
Asymptotic Properties
Bootstrap Tests
Density Estimation
Hypothesis Testing
Maximum Likelihood Estimators
Spherical Data
Von Mises Distribution
title_short Goodness-of-fit test for directional data
title_full Goodness-of-fit test for directional data
title_fullStr Goodness-of-fit test for directional data
title_full_unstemmed Goodness-of-fit test for directional data
title_sort Goodness-of-fit test for directional data
dc.creator.none.fl_str_mv Boente Boente, Graciela Lina
González Manteiga, Wenceslao
Rodriguez, Daniela Andrea
author Boente Boente, Graciela Lina
author_facet Boente Boente, Graciela Lina
González Manteiga, Wenceslao
Rodriguez, Daniela Andrea
author_role author
author2 González Manteiga, Wenceslao
Rodriguez, Daniela Andrea
author2_role author
author
dc.subject.none.fl_str_mv Asymptotic Properties
Bootstrap Tests
Density Estimation
Hypothesis Testing
Maximum Likelihood Estimators
Spherical Data
Von Mises Distribution
topic Asymptotic Properties
Bootstrap Tests
Density Estimation
Hypothesis Testing
Maximum Likelihood Estimators
Spherical Data
Von Mises 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 In this paper, we study the problem of testing the hypothesis on whether the density f of a random variable on a sphere belongs to a given parametric class of densities. We propose two test statistics based on the L2 and L1 distances between a non-parametric density estimator adapted to circular data and a smoothed version of the specified density. The asymptotic distribution of the L2 test statistic is provided under the null hypothesis and contiguous alternatives. We also consider a bootstrap method to approximate the distribution of both test statistics. Through a simulation study, we explore the moderate sample performance of the proposed tests under the null hypothesis and under different alternatives. Finally, the procedure is illustrated by analysing a real data set based on wind direction measurements.
Fil: Boente Boente, Graciela Lina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas ; Argentina
Fil: González Manteiga, Wenceslao. Universidad de Santiago de Compostela; España
Fil: Rodriguez, Daniela Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas ; Argentina
description In this paper, we study the problem of testing the hypothesis on whether the density f of a random variable on a sphere belongs to a given parametric class of densities. We propose two test statistics based on the L2 and L1 distances between a non-parametric density estimator adapted to circular data and a smoothed version of the specified density. The asymptotic distribution of the L2 test statistic is provided under the null hypothesis and contiguous alternatives. We also consider a bootstrap method to approximate the distribution of both test statistics. Through a simulation study, we explore the moderate sample performance of the proposed tests under the null hypothesis and under different alternatives. Finally, the procedure is illustrated by analysing a real data set based on wind direction measurements.
publishDate 2014
dc.date.none.fl_str_mv 2014-03
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/11336/37348
Boente Boente, Graciela Lina; González Manteiga, Wenceslao; Rodriguez, Daniela Andrea; Goodness-of-fit test for directional data; Wiley Blackwell Publishing, Inc; Scandinavian Journal Of Statistics; 41; 1; 3-2014; 259-275
0303-6898
CONICET Digital
CONICET
url http://hdl.handle.net/11336/37348
identifier_str_mv Boente Boente, Graciela Lina; González Manteiga, Wenceslao; Rodriguez, Daniela Andrea; Goodness-of-fit test for directional data; Wiley Blackwell Publishing, Inc; Scandinavian Journal Of Statistics; 41; 1; 3-2014; 259-275
0303-6898
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://onlinelibrary.wiley.com/doi/10.1111/sjos.12020/abstract
info:eu-repo/semantics/altIdentifier/doi/10.1111/sjos.12020
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
application/pdf
dc.publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
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_ 1844613149258416128
score 13.070432

Publicaciones similares