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
.jpg)
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/37348
Ver los metadatos del registro completo
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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 Técnicas2025-10-22T11:04:43Zoai: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:34982025-10-22 11:04:43.69CONICET 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 |
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2014-03 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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article |
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publishedVersion |
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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 |
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eng |
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eng |
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