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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9778345 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT kertelmaximilian estimatinggaussiancopulaswithmissingdatawithandwithoutexpertknowledge AT paulymarkus estimatinggaussiancopulaswithmissingdatawithandwithoutexpertknowledge |