Model Selection in a Composite Likelihood Framework Based on Density Power Divergence

This paper presents a model selection criterion in a composite likelihood framework based on density power divergence measures and in the composite minimum density power divergence estimators, which depends on an tuning parameter [Formula: see text]. After introducing such a criterion, some asymptot...

Descripción completa

Detalles Bibliográficos
Autores principales: Castilla, Elena, Martín, Nirian, Pardo, Leandro, Zografos, Konstantinos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516723/
https://www.ncbi.nlm.nih.gov/pubmed/33286044
http://dx.doi.org/10.3390/e22030270
_version_ 1783587066982957056
author Castilla, Elena
Martín, Nirian
Pardo, Leandro
Zografos, Konstantinos
author_facet Castilla, Elena
Martín, Nirian
Pardo, Leandro
Zografos, Konstantinos
author_sort Castilla, Elena
collection PubMed
description This paper presents a model selection criterion in a composite likelihood framework based on density power divergence measures and in the composite minimum density power divergence estimators, which depends on an tuning parameter [Formula: see text]. After introducing such a criterion, some asymptotic properties are established. We present a simulation study and two numerical examples in order to point out the robustness properties of the introduced model selection criterion.
format Online
Article
Text
id pubmed-7516723
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-75167232020-11-09 Model Selection in a Composite Likelihood Framework Based on Density Power Divergence Castilla, Elena Martín, Nirian Pardo, Leandro Zografos, Konstantinos Entropy (Basel) Article This paper presents a model selection criterion in a composite likelihood framework based on density power divergence measures and in the composite minimum density power divergence estimators, which depends on an tuning parameter [Formula: see text]. After introducing such a criterion, some asymptotic properties are established. We present a simulation study and two numerical examples in order to point out the robustness properties of the introduced model selection criterion. MDPI 2020-02-27 /pmc/articles/PMC7516723/ /pubmed/33286044 http://dx.doi.org/10.3390/e22030270 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Castilla, Elena
Martín, Nirian
Pardo, Leandro
Zografos, Konstantinos
Model Selection in a Composite Likelihood Framework Based on Density Power Divergence
title Model Selection in a Composite Likelihood Framework Based on Density Power Divergence
title_full Model Selection in a Composite Likelihood Framework Based on Density Power Divergence
title_fullStr Model Selection in a Composite Likelihood Framework Based on Density Power Divergence
title_full_unstemmed Model Selection in a Composite Likelihood Framework Based on Density Power Divergence
title_short Model Selection in a Composite Likelihood Framework Based on Density Power Divergence
title_sort model selection in a composite likelihood framework based on density power divergence
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516723/
https://www.ncbi.nlm.nih.gov/pubmed/33286044
http://dx.doi.org/10.3390/e22030270
work_keys_str_mv AT castillaelena modelselectioninacompositelikelihoodframeworkbasedondensitypowerdivergence
AT martinnirian modelselectioninacompositelikelihoodframeworkbasedondensitypowerdivergence
AT pardoleandro modelselectioninacompositelikelihoodframeworkbasedondensitypowerdivergence
AT zografoskonstantinos modelselectioninacompositelikelihoodframeworkbasedondensitypowerdivergence

Ejemplares similares