Multiple robustness in factorized likelihood models

Autores
Molina, J.; Rotnitzky, Andrea Gloria; Sued, Raquel Mariela; Robins, J. M.
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors.We are interested in a finitedimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires postulating several dimension-reducing models but which have mean zero at the true parameter value provided one of these models is correct.
Fil: Molina, J.. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina
Fil: Rotnitzky, Andrea Gloria. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Robins, J. M.. Harvard University; Estados Unidos
Materia
Causal Inference
Estimating Function
Missing Data
Semiparametric Model
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:11336/60036
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Multiple robustness in factorized likelihood modelsMolina, J.Rotnitzky, Andrea GloriaSued, Raquel MarielaRobins, J. M.Causal InferenceEstimating FunctionMissing DataSemiparametric Modelhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors.We are interested in a finitedimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires postulating several dimension-reducing models but which have mean zero at the true parameter value provided one of these models is correct.Fil: Molina, J.. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; ArgentinaFil: Rotnitzky, Andrea Gloria. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Robins, J. M.. Harvard University; Estados UnidosOxford University Press2017-09info: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/11336/60036Molina, J.; Rotnitzky, Andrea Gloria; Sued, Raquel Mariela; Robins, J. M.; Multiple robustness in factorized likelihood models; Oxford University Press; Biometrika; 104; 3; 9-2017; 561-5810006-3444CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1093/biomet/asx027info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/biomet/article/104/3/561/3868976info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:04:15Zoai:ri.conicet.gov.ar:11336/60036instacron: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 12:04:15.581CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Multiple robustness in factorized likelihood models
title Multiple robustness in factorized likelihood models
spellingShingle Multiple robustness in factorized likelihood models
Molina, J.
Causal Inference
Estimating Function
Missing Data
Semiparametric Model
title_short Multiple robustness in factorized likelihood models
title_full Multiple robustness in factorized likelihood models
title_fullStr Multiple robustness in factorized likelihood models
title_full_unstemmed Multiple robustness in factorized likelihood models
title_sort Multiple robustness in factorized likelihood models
dc.creator.none.fl_str_mv Molina, J.
Rotnitzky, Andrea Gloria
Sued, Raquel Mariela
Robins, J. M.
author Molina, J.
author_facet Molina, J.
Rotnitzky, Andrea Gloria
Sued, Raquel Mariela
Robins, J. M.
author_role author
author2 Rotnitzky, Andrea Gloria
Sued, Raquel Mariela
Robins, J. M.
author2_role author
author
author
dc.subject.none.fl_str_mv Causal Inference
Estimating Function
Missing Data
Semiparametric Model
topic Causal Inference
Estimating Function
Missing Data
Semiparametric Model
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors.We are interested in a finitedimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires postulating several dimension-reducing models but which have mean zero at the true parameter value provided one of these models is correct.
Fil: Molina, J.. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina
Fil: Rotnitzky, Andrea Gloria. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Robins, J. M.. Harvard University; Estados Unidos
description We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors.We are interested in a finitedimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires postulating several dimension-reducing models but which have mean zero at the true parameter value provided one of these models is correct.
publishDate 2017
dc.date.none.fl_str_mv 2017-09
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/11336/60036
Molina, J.; Rotnitzky, Andrea Gloria; Sued, Raquel Mariela; Robins, J. M.; Multiple robustness in factorized likelihood models; Oxford University Press; Biometrika; 104; 3; 9-2017; 561-581
0006-3444
CONICET Digital
CONICET
url http://hdl.handle.net/11336/60036
identifier_str_mv Molina, J.; Rotnitzky, Andrea Gloria; Sued, Raquel Mariela; Robins, J. M.; Multiple robustness in factorized likelihood models; Oxford University Press; Biometrika; 104; 3; 9-2017; 561-581
0006-3444
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1093/biomet/asx027
info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/biomet/article/104/3/561/3868976
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 Oxford University Press
publisher.none.fl_str_mv Oxford University Press
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 12.982451

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