Partly linear models on Riemannian manifolds

Autores
González Manteiga, Wenceslao; Henry, Guillermo Sebastian; Rodriguez, Daniela Andrea
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In partly linear models, the dependence of the response y on (xT, t) is modeled through the relationship y = xTβ + g(t) + ε, where ε is independent of (xT, t). We are interested in developing an estimation procedure that allows us to combine the flexibility of the partly linear models, studied by several authors, but including some variables that belong to a non-Euclidean space. The motivating application of this paper deals with the explanation of the atmospheric SO2 pollution incidents using these models when some of the predictive variables belong in a cylinder. In this paper, the estimators of β and g are constructed when the explanatory variablest take values on a Riemannian manifold and the asymptotic properties of the proposed estimators are obtained under suitable conditions. We illustrate the use of this estimation approach using an environmental data set and we explore the performance of the estimators through a simulation study.
Fil: González Manteiga, Wenceslao. Universidad de Santiago de Compostela; España
Fil: Henry, Guillermo Sebastian. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rodriguez, Daniela Andrea. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Hypothesis Test
Nonparametric Estimation
Partly Linear Models
Riemannian Manifolds
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:11336/19993
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Partly linear models on Riemannian manifoldsGonzález Manteiga, WenceslaoHenry, Guillermo SebastianRodriguez, Daniela AndreaHypothesis TestNonparametric EstimationPartly Linear ModelsRiemannian Manifoldshttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1In partly linear models, the dependence of the response y on (xT, t) is modeled through the relationship y = xTβ + g(t) + ε, where ε is independent of (xT, t). We are interested in developing an estimation procedure that allows us to combine the flexibility of the partly linear models, studied by several authors, but including some variables that belong to a non-Euclidean space. The motivating application of this paper deals with the explanation of the atmospheric SO2 pollution incidents using these models when some of the predictive variables belong in a cylinder. In this paper, the estimators of β and g are constructed when the explanatory variablest take values on a Riemannian manifold and the asymptotic properties of the proposed estimators are obtained under suitable conditions. We illustrate the use of this estimation approach using an environmental data set and we explore the performance of the estimators through a simulation study.Fil: González Manteiga, Wenceslao. Universidad de Santiago de Compostela; EspañaFil: Henry, Guillermo Sebastian. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rodriguez, Daniela Andrea. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaTaylor & Francis2012-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/19993González Manteiga, Wenceslao; Henry, Guillermo Sebastian; Rodriguez, Daniela Andrea; Partly linear models on Riemannian manifolds; Taylor & Francis; Journal of Applied Statistics; 39; 8; 5-2012; 1797-18090266-47631360-0532CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1080/02664763.2012.683169info:eu-repo/semantics/altIdentifier/url/http://www.tandfonline.com/doi/abs/10.1080/02664763.2012.683169info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1003.1573info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:20:05Zoai:ri.conicet.gov.ar:11336/19993instacron: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:34982025-09-10 13:20:06.137CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Partly linear models on Riemannian manifolds
title Partly linear models on Riemannian manifolds
spellingShingle Partly linear models on Riemannian manifolds
González Manteiga, Wenceslao
Hypothesis Test
Nonparametric Estimation
Partly Linear Models
Riemannian Manifolds
title_short Partly linear models on Riemannian manifolds
title_full Partly linear models on Riemannian manifolds
title_fullStr Partly linear models on Riemannian manifolds
title_full_unstemmed Partly linear models on Riemannian manifolds
title_sort Partly linear models on Riemannian manifolds
dc.creator.none.fl_str_mv González Manteiga, Wenceslao
Henry, Guillermo Sebastian
Rodriguez, Daniela Andrea
author González Manteiga, Wenceslao
author_facet González Manteiga, Wenceslao
Henry, Guillermo Sebastian
Rodriguez, Daniela Andrea
author_role author
author2 Henry, Guillermo Sebastian
Rodriguez, Daniela Andrea
author2_role author
author
dc.subject.none.fl_str_mv Hypothesis Test
Nonparametric Estimation
Partly Linear Models
Riemannian Manifolds
topic Hypothesis Test
Nonparametric Estimation
Partly Linear Models
Riemannian Manifolds
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 partly linear models, the dependence of the response y on (xT, t) is modeled through the relationship y = xTβ + g(t) + ε, where ε is independent of (xT, t). We are interested in developing an estimation procedure that allows us to combine the flexibility of the partly linear models, studied by several authors, but including some variables that belong to a non-Euclidean space. The motivating application of this paper deals with the explanation of the atmospheric SO2 pollution incidents using these models when some of the predictive variables belong in a cylinder. In this paper, the estimators of β and g are constructed when the explanatory variablest take values on a Riemannian manifold and the asymptotic properties of the proposed estimators are obtained under suitable conditions. We illustrate the use of this estimation approach using an environmental data set and we explore the performance of the estimators through a simulation study.
Fil: González Manteiga, Wenceslao. Universidad de Santiago de Compostela; España
Fil: Henry, Guillermo Sebastian. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rodriguez, Daniela Andrea. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description In partly linear models, the dependence of the response y on (xT, t) is modeled through the relationship y = xTβ + g(t) + ε, where ε is independent of (xT, t). We are interested in developing an estimation procedure that allows us to combine the flexibility of the partly linear models, studied by several authors, but including some variables that belong to a non-Euclidean space. The motivating application of this paper deals with the explanation of the atmospheric SO2 pollution incidents using these models when some of the predictive variables belong in a cylinder. In this paper, the estimators of β and g are constructed when the explanatory variablest take values on a Riemannian manifold and the asymptotic properties of the proposed estimators are obtained under suitable conditions. We illustrate the use of this estimation approach using an environmental data set and we explore the performance of the estimators through a simulation study.
publishDate 2012
dc.date.none.fl_str_mv 2012-05
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/11336/19993
González Manteiga, Wenceslao; Henry, Guillermo Sebastian; Rodriguez, Daniela Andrea; Partly linear models on Riemannian manifolds; Taylor & Francis; Journal of Applied Statistics; 39; 8; 5-2012; 1797-1809
0266-4763
1360-0532
CONICET Digital
CONICET
url http://hdl.handle.net/11336/19993
identifier_str_mv González Manteiga, Wenceslao; Henry, Guillermo Sebastian; Rodriguez, Daniela Andrea; Partly linear models on Riemannian manifolds; Taylor & Francis; Journal of Applied Statistics; 39; 8; 5-2012; 1797-1809
0266-4763
1360-0532
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1080/02664763.2012.683169
info:eu-repo/semantics/altIdentifier/url/http://www.tandfonline.com/doi/abs/10.1080/02664763.2012.683169
info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1003.1573
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 Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
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
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score 12.48226

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