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Estimating Gaussian Copulas with Missing Data with and without Expert Knowledge

In this work, we present a rigorous application of the Expectation Maximization algorithm to determine the marginal distributions and the dependence structure in a Gaussian copula model with missing data. We further show how to circumvent a priori assumptions on the marginals with semiparametric mod...

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Detalles Bibliográficos
Autores principales: Kertel, Maximilian, Pauly, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778345/
https://www.ncbi.nlm.nih.gov/pubmed/36554254
http://dx.doi.org/10.3390/e24121849
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author Kertel, Maximilian
Pauly, Markus
author_facet Kertel, Maximilian
Pauly, Markus
author_sort Kertel, Maximilian
collection PubMed
description In this work, we present a rigorous application of the Expectation Maximization algorithm to determine the marginal distributions and the dependence structure in a Gaussian copula model with missing data. We further show how to circumvent a priori assumptions on the marginals with semiparametric modeling. Further, we outline how expert knowledge on the marginals and the dependency structure can be included. A simulation study shows that the distribution learned through this algorithm is closer to the true distribution than that obtained with existing methods and that the incorporation of domain knowledge provides benefits.
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spelling pubmed-97783452022-12-23 Estimating Gaussian Copulas with Missing Data with and without Expert Knowledge Kertel, Maximilian Pauly, Markus Entropy (Basel) Article In this work, we present a rigorous application of the Expectation Maximization algorithm to determine the marginal distributions and the dependence structure in a Gaussian copula model with missing data. We further show how to circumvent a priori assumptions on the marginals with semiparametric modeling. Further, we outline how expert knowledge on the marginals and the dependency structure can be included. A simulation study shows that the distribution learned through this algorithm is closer to the true distribution than that obtained with existing methods and that the incorporation of domain knowledge provides benefits. MDPI 2022-12-19 /pmc/articles/PMC9778345/ /pubmed/36554254 http://dx.doi.org/10.3390/e24121849 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kertel, Maximilian
Pauly, Markus
Estimating Gaussian Copulas with Missing Data with and without Expert Knowledge
title Estimating Gaussian Copulas with Missing Data with and without Expert Knowledge
title_full Estimating Gaussian Copulas with Missing Data with and without Expert Knowledge
title_fullStr Estimating Gaussian Copulas with Missing Data with and without Expert Knowledge
title_full_unstemmed Estimating Gaussian Copulas with Missing Data with and without Expert Knowledge
title_short Estimating Gaussian Copulas with Missing Data with and without Expert Knowledge
title_sort estimating gaussian copulas with missing data with and without expert knowledge
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778345/
https://www.ncbi.nlm.nih.gov/pubmed/36554254
http://dx.doi.org/10.3390/e24121849
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