Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution
Identifieur interne : 009589 ( Main/Curation ); précédent : 009588; suivant : 009590Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution
Auteurs : G. J. Mclachlan [Australie] ; R. W. Bean [Australie] ; L. Ben-Tovim Jones [Australie]Source :
- Computational statistics & data analysis [ 0167-9473 ] ; 2007.
Descripteurs français
- Pascal (Inist)
- Mélange loi probabilité, Analyse multivariable, Loi n variables, Fonction répartition, Estimation densité, Donnée observation, Observation aberrante, Loi normale, Estimation erreur, Algorithme EM, Ajustement, Classification automatique (statistiques), Analyse donnée, Calcul statistique, 62H10, 60E05, 62G07, 62H30, 62F03, Modèle factoriel, Puce à ADN.
English descriptors
- KwdEn :
- Cluster analysis (statistics), Data analysis, Density estimation, Distribution function, EM algorithm, Error estimation, Factor model, Fitting, Gaussian distribution, Microarray, Mixed distribution, Multivariate analysis, Multivariate distribution, Observation data, Outlier, Statistical computation.
Abstract
Mixtures of factor analyzers enable model-based density estimation to be undertaken for high-dimensional data, where the number of observations n is small relative to their dimension p. However, this approach is sensitive to outliers as it is based on a mixture model in which the multivariate normal family of distributions is assumed for the component error and factor distributions. An extension to mixtures of t-factor analyzers is considered, whereby the multivariate t-family is adopted for the component error and factor distributions. An EM-based algorithm is developed for the fitting of mixtures of t-factor analyzers. Its application is demonstrated in the clustering of some microarray gene-expression data.
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Pascal:07-0325488Le document en format XML
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<term>Estimation densité</term>
<term>Donnée observation</term>
<term>Observation aberrante</term>
<term>Loi normale</term>
<term>Estimation erreur</term>
<term>Algorithme EM</term>
<term>Ajustement</term>
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<term>Analyse donnée</term>
<term>Calcul statistique</term>
<term>62H10</term>
<term>60E05</term>
<term>62G07</term>
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<front><div type="abstract" xml:lang="en">Mixtures of factor analyzers enable model-based density estimation to be undertaken for high-dimensional data, where the number of observations n is small relative to their dimension p. However, this approach is sensitive to outliers as it is based on a mixture model in which the multivariate normal family of distributions is assumed for the component error and factor distributions. An extension to mixtures of t-factor analyzers is considered, whereby the multivariate t-family is adopted for the component error and factor distributions. An EM-based algorithm is developed for the fitting of mixtures of t-factor analyzers. Its application is demonstrated in the clustering of some microarray gene-expression data.</div>
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