Tests for Gaussian graphical models
Identifieur interne : 008051 ( Main/Exploration ); précédent : 008050; suivant : 008052Tests for Gaussian graphical models
Auteurs : N. Verzelen [France] ; F. Villers [France]Source :
- Computational statistics & data analysis [ 0167-9473 ] ; 2009.
Descripteurs français
- Pascal (Inist)
- Wicri :
- topic : Génétique.
English descriptors
- KwdEn :
Abstract
Gaussian graphical models are promising tools for analysing genetic networks. In many applications, biologists have some knowledge of the genetic network and may want to assess the quality of their model using gene expression data. This is why one introduces a novel procedure for testing the neighborhoods of a Gaussian graphical model. It is based on the connection between the local Markov property and conditional regression of a Gaussian random variable. Adapting recent results on tests for high-dimensional Gaussian linear models, one proves that the testing procedure inherits appealing theoretical properties. Besides, it applies and is computationally feasible in a high-dimensional setting: the number of nodes may be much larger than the number of observations. A large part of the study is devoted to illustrating and discussing applications to simulated data and to biological data.
Affiliations:
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Villers</name><affiliation wicri:level="3"><inist:fA14 i1="03"><s1>INRA, Mathématiques et Informatique Appliquées MIA</s1><s2>78352 Jouy-en-Josas</s2><s3>FRA</s3><sZ>2 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Île-de-France</region><settlement type="city">Jouy-en-Josas</settlement></placeName></affiliation></author></analytic><series><title level="j" type="main">Computational statistics & data analysis</title><title level="j" type="abbreviated">Comput. stat. data anal.</title><idno type="ISSN">0167-9473</idno><imprint><date when="2009">2009</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Computational statistics & data analysis</title><title level="j" type="abbreviated">Comput. stat. data anal.</title><idno type="ISSN">0167-9473</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Conditional distribution</term><term>Data analysis</term><term>Genetics</term><term>Graphic method</term><term>Graphical model</term><term>Linear model</term><term>Random variable</term><term>Statistical computation</term><term>Statistical regression</term><term>Statistical test</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Méthode graphique</term><term>Génétique</term><term>Test statistique</term><term>Loi conditionnelle</term><term>Régression statistique</term><term>Variable aléatoire</term><term>Modèle linéaire</term><term>Analyse donnée</term><term>Calcul statistique</term><term>62-09</term><term>62Jxx</term><term>Modèle graphique</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Génétique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Gaussian graphical models are promising tools for analysing genetic networks. In many applications, biologists have some knowledge of the genetic network and may want to assess the quality of their model using gene expression data. This is why one introduces a novel procedure for testing the neighborhoods of a Gaussian graphical model. It is based on the connection between the local Markov property and conditional regression of a Gaussian random variable. Adapting recent results on tests for high-dimensional Gaussian linear models, one proves that the testing procedure inherits appealing theoretical properties. Besides, it applies and is computationally feasible in a high-dimensional setting: the number of nodes may be much larger than the number of observations. A large part of the study is devoted to illustrating and discussing applications to simulated data and to biological data.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Île-de-France</li></region><settlement><li>Jouy-en-Josas</li><li>Orsay</li></settlement><orgName><li>Université Paris-Sud</li></orgName></list><tree><country name="France"><region name="Île-de-France"><name sortKey="Verzelen, N" sort="Verzelen, N" uniqKey="Verzelen N" first="N." last="Verzelen">N. Verzelen</name></region><name sortKey="Verzelen, N" sort="Verzelen, N" uniqKey="Verzelen N" first="N." last="Verzelen">N. Verzelen</name><name sortKey="Villers, F" sort="Villers, F" uniqKey="Villers F" first="F." last="Villers">F. Villers</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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