Restricted distance-type Gaussian estimators based on density power divergence and their aplications in hypothesis testing

In this paper, we introduce the restricted minimum density power divergence Gaussian estimator (MDPDGE) and study its main asymptotic properties. In addition, we examine it robustness through its influence function analysis. Restricted estimators are required in many practical situations, such as te...

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Detalles Bibliográficos
Autores: Felipe Ortega, Ángel, Jaenada Malagón, María, Miranda Menéndez, Pedro, Pardo Llorente, Leandro
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/72964
Acceso en línea:https://hdl.handle.net/20.500.14352/72964
Access Level:acceso abierto
Palabra clave:519.22
Gaussian estimator
Minimum density power divergence Gaussian estimator
Robustness
Influence function
Rao-type tests
Elliptical family of distributions
Estadística matemática (Matemáticas)
1209 Estadística
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oai_identifier_str oai:docta.ucm.es:20.500.14352/72964
network_acronym_str ES
network_name_str España
repository_id_str
spelling Restricted distance-type Gaussian estimators based on density power divergence and their aplications in hypothesis testingFelipe Ortega, ÁngelJaenada Malagón, MaríaMiranda Menéndez, PedroPardo Llorente, Leandro519.22Gaussian estimatorMinimum density power divergence Gaussian estimatorRobustnessInfluence functionRao-type testsElliptical family of distributionsEstadística matemática (Matemáticas)1209 EstadísticaIn this paper, we introduce the restricted minimum density power divergence Gaussian estimator (MDPDGE) and study its main asymptotic properties. In addition, we examine it robustness through its influence function analysis. Restricted estimators are required in many practical situations, such as testing composite null hypotheses, and we provide in this case constrained estimators to inherent restrictions of the underlying distribution. Furthermore, we derive robust Rao-type test statistics based on the MDPDGE for testing a simple null hypothesis, and we deduce explicit expressions for some main important distributions. Finally, we empirically evaluate the efficiency and robustness of the method through a simulation studyMDPIUniversidad Complutense de Madrid20232023-03-1720232023-03-17journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/72964reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución 3.0 Españahttps://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/729642026-06-02T12:44:21Z
dc.title.none.fl_str_mv Restricted distance-type Gaussian estimators based on density power divergence and their aplications in hypothesis testing
title Restricted distance-type Gaussian estimators based on density power divergence and their aplications in hypothesis testing
spellingShingle Restricted distance-type Gaussian estimators based on density power divergence and their aplications in hypothesis testing
Felipe Ortega, Ángel
519.22
Gaussian estimator
Minimum density power divergence Gaussian estimator
Robustness
Influence function
Rao-type tests
Elliptical family of distributions
Estadística matemática (Matemáticas)
1209 Estadística
title_short Restricted distance-type Gaussian estimators based on density power divergence and their aplications in hypothesis testing
title_full Restricted distance-type Gaussian estimators based on density power divergence and their aplications in hypothesis testing
title_fullStr Restricted distance-type Gaussian estimators based on density power divergence and their aplications in hypothesis testing
title_full_unstemmed Restricted distance-type Gaussian estimators based on density power divergence and their aplications in hypothesis testing
title_sort Restricted distance-type Gaussian estimators based on density power divergence and their aplications in hypothesis testing
dc.creator.none.fl_str_mv Felipe Ortega, Ángel
Jaenada Malagón, María
Miranda Menéndez, Pedro
Pardo Llorente, Leandro
author Felipe Ortega, Ángel
author_facet Felipe Ortega, Ángel
Jaenada Malagón, María
Miranda Menéndez, Pedro
Pardo Llorente, Leandro
author_role author
author2 Jaenada Malagón, María
Miranda Menéndez, Pedro
Pardo Llorente, Leandro
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 519.22
Gaussian estimator
Minimum density power divergence Gaussian estimator
Robustness
Influence function
Rao-type tests
Elliptical family of distributions
Estadística matemática (Matemáticas)
1209 Estadística
topic 519.22
Gaussian estimator
Minimum density power divergence Gaussian estimator
Robustness
Influence function
Rao-type tests
Elliptical family of distributions
Estadística matemática (Matemáticas)
1209 Estadística
description In this paper, we introduce the restricted minimum density power divergence Gaussian estimator (MDPDGE) and study its main asymptotic properties. In addition, we examine it robustness through its influence function analysis. Restricted estimators are required in many practical situations, such as testing composite null hypotheses, and we provide in this case constrained estimators to inherent restrictions of the underlying distribution. Furthermore, we derive robust Rao-type test statistics based on the MDPDGE for testing a simple null hypothesis, and we deduce explicit expressions for some main important distributions. Finally, we empirically evaluate the efficiency and robustness of the method through a simulation study
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-03-17
2023
2023-03-17
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/72964
url https://hdl.handle.net/20.500.14352/72964
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución 3.0 España
https://creativecommons.org/licenses/by/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución 3.0 España
https://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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