Iterative reconstruction of high-dimensional Gaussian Graphical Models based on a new method to estimate partial correlations under constraints.

In the context of Gaussian Graphical Models (GGMs) with high-dimensional small sample data, we present a simple procedure, called PACOSE - standing for PArtial COrrelation SElection - to estimate partial correlations under the constraint that some of them are strictly zero. This method can also be e...

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Autores principales:	Vincent Guillemot, Andreas Bender, Anne-Laure Boulesteix
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a0eb6a87f8a6470f890a0844907b465d
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spelling	oai:doaj.org-article:a0eb6a87f8a6470f890a0844907b465d2021-11-18T07:49:47ZIterative reconstruction of high-dimensional Gaussian Graphical Models based on a new method to estimate partial correlations under constraints.1932-620310.1371/journal.pone.0060536https://doaj.org/article/a0eb6a87f8a6470f890a0844907b465d2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23593235/?tool=EBIhttps://doaj.org/toc/1932-6203In the context of Gaussian Graphical Models (GGMs) with high-dimensional small sample data, we present a simple procedure, called PACOSE - standing for PArtial COrrelation SElection - to estimate partial correlations under the constraint that some of them are strictly zero. This method can also be extended to covariance selection. If the goal is to estimate a GGM, our new procedure can be applied to re-estimate the partial correlations after a first graph has been estimated in the hope to improve the estimation of non-zero coefficients. This iterated version of PACOSE is called iPACOSE. In a simulation study, we compare PACOSE to existing methods and show that the re-estimated partial correlation coefficients may be closer to the real values in important cases. Plus, we show on simulated and real data that iPACOSE shows very interesting properties with regards to sensitivity, positive predictive value and stability.Vincent GuillemotAndreas BenderAnne-Laure BoulesteixPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 4, p e60536 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Vincent Guillemot Andreas Bender Anne-Laure Boulesteix Iterative reconstruction of high-dimensional Gaussian Graphical Models based on a new method to estimate partial correlations under constraints.
description	In the context of Gaussian Graphical Models (GGMs) with high-dimensional small sample data, we present a simple procedure, called PACOSE - standing for PArtial COrrelation SElection - to estimate partial correlations under the constraint that some of them are strictly zero. This method can also be extended to covariance selection. If the goal is to estimate a GGM, our new procedure can be applied to re-estimate the partial correlations after a first graph has been estimated in the hope to improve the estimation of non-zero coefficients. This iterated version of PACOSE is called iPACOSE. In a simulation study, we compare PACOSE to existing methods and show that the re-estimated partial correlation coefficients may be closer to the real values in important cases. Plus, we show on simulated and real data that iPACOSE shows very interesting properties with regards to sensitivity, positive predictive value and stability.
format	article
author	Vincent Guillemot Andreas Bender Anne-Laure Boulesteix
author_facet	Vincent Guillemot Andreas Bender Anne-Laure Boulesteix
author_sort	Vincent Guillemot
title	Iterative reconstruction of high-dimensional Gaussian Graphical Models based on a new method to estimate partial correlations under constraints.
title_short	Iterative reconstruction of high-dimensional Gaussian Graphical Models based on a new method to estimate partial correlations under constraints.
title_full	Iterative reconstruction of high-dimensional Gaussian Graphical Models based on a new method to estimate partial correlations under constraints.
title_fullStr	Iterative reconstruction of high-dimensional Gaussian Graphical Models based on a new method to estimate partial correlations under constraints.
title_full_unstemmed	Iterative reconstruction of high-dimensional Gaussian Graphical Models based on a new method to estimate partial correlations under constraints.
title_sort	iterative reconstruction of high-dimensional gaussian graphical models based on a new method to estimate partial correlations under constraints.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/a0eb6a87f8a6470f890a0844907b465d
work_keys_str_mv	AT vincentguillemot iterativereconstructionofhighdimensionalgaussiangraphicalmodelsbasedonanewmethodtoestimatepartialcorrelationsunderconstraints AT andreasbender iterativereconstructionofhighdimensionalgaussiangraphicalmodelsbasedonanewmethodtoestimatepartialcorrelationsunderconstraints AT annelaureboulesteix iterativereconstructionofhighdimensionalgaussiangraphicalmodelsbasedonanewmethodtoestimatepartialcorrelationsunderconstraints
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Iterative reconstruction of high-dimensional Gaussian Graphical Models based on a new method to estimate partial correlations under constraints.

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