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Mixture prior for sparse signals with dependent covariance structure

In this study, we propose an estimation method for normal mean problem that can have unknown sparsity as well as correlations in the signals. Our proposed method first decomposes arbitrary dependent covariance matrix of the observed signals into two parts: common dependence and weakly dependent erro...

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Detalles Bibliográficos
Autores principales: Wang, Ling, Liao, Zongqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10138223/
https://www.ncbi.nlm.nih.gov/pubmed/37104465
http://dx.doi.org/10.1371/journal.pone.0284284
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author Wang, Ling
Liao, Zongqiang
author_facet Wang, Ling
Liao, Zongqiang
author_sort Wang, Ling
collection PubMed
description In this study, we propose an estimation method for normal mean problem that can have unknown sparsity as well as correlations in the signals. Our proposed method first decomposes arbitrary dependent covariance matrix of the observed signals into two parts: common dependence and weakly dependent error terms. By subtracting common dependence, the correlations among the signals are significantly weakened. It is practical for doing this because of the existence of sparsity. Then the sparsity is estimated using an empirical Bayesian method based on the likelihood of the signals with the common dependence removed. Using simulated examples that have moderate to high degrees of sparsity and different dependent structures in the signals, we demonstrate that the performance of our proposed algorithm is favorable compared to the existing method which assumes the signals are independent identically distributed. Furthermore, our approach is applied on the widely used “Hapmap” gene expressions data, and our results are consistent with the findings in other studies.
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spelling pubmed-101382232023-04-28 Mixture prior for sparse signals with dependent covariance structure Wang, Ling Liao, Zongqiang PLoS One Research Article In this study, we propose an estimation method for normal mean problem that can have unknown sparsity as well as correlations in the signals. Our proposed method first decomposes arbitrary dependent covariance matrix of the observed signals into two parts: common dependence and weakly dependent error terms. By subtracting common dependence, the correlations among the signals are significantly weakened. It is practical for doing this because of the existence of sparsity. Then the sparsity is estimated using an empirical Bayesian method based on the likelihood of the signals with the common dependence removed. Using simulated examples that have moderate to high degrees of sparsity and different dependent structures in the signals, we demonstrate that the performance of our proposed algorithm is favorable compared to the existing method which assumes the signals are independent identically distributed. Furthermore, our approach is applied on the widely used “Hapmap” gene expressions data, and our results are consistent with the findings in other studies. Public Library of Science 2023-04-27 /pmc/articles/PMC10138223/ /pubmed/37104465 http://dx.doi.org/10.1371/journal.pone.0284284 Text en © 2023 Wang, Liao https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wang, Ling
Liao, Zongqiang
Mixture prior for sparse signals with dependent covariance structure
title Mixture prior for sparse signals with dependent covariance structure
title_full Mixture prior for sparse signals with dependent covariance structure
title_fullStr Mixture prior for sparse signals with dependent covariance structure
title_full_unstemmed Mixture prior for sparse signals with dependent covariance structure
title_short Mixture prior for sparse signals with dependent covariance structure
title_sort mixture prior for sparse signals with dependent covariance structure
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10138223/
https://www.ncbi.nlm.nih.gov/pubmed/37104465
http://dx.doi.org/10.1371/journal.pone.0284284
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