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- W2015220578 abstract "We propose a new type of multivariate statistical model that permits non-Gaussian distributions as well as the inclusion of conditional independence assumptions specified by a directed acyclic graph. These models feature a specific factorisation of the likelihood that is based on pair-copula constructions and hence involves only univariate distributions and bivariate copulas, of which some may be conditional. We demonstrate maximum-likelihood estimation of the parameters of such models and compare them to various competing models from the literature. A simulation study investigates the effects of model misspecification and highlights the need for non-Gaussian conditional independence models. The proposed methods are finally applied to modeling financial return data. The Canadian Journal of Statistics 40: 86–109; 2012 © 2012 Statistical Society of Canada Nous proposons un nouveau type de modèle statistique multidimensionnel qui permet aussi bien les distributions non gaussiennes que l'inclusion d'hypothèse d'indépendance conditionnelle spécifié par un graphe orienté acyclique. Ces modèles permettent une factorisation spécifique de la vraisemblance basée sur des constructions des copules bidimensionnelles. Par conséquent, elle n'implique que des densités unidimensionnelles et des copules bidimensionnelles parmi lesquelles quelques-unes peuvent être conditionnelles. Nous obtenons les estimateurs du maximum de vraisemblance des paramètres de ces modèles et nous les comparons aux différents modèles concurrents trouvés dans la littérature. Une étude de simulation étudie les effets d'une mauvaise spécification du modèle et elle met en évidence le besoin de modèles non gaussiens d'indépendance conditionnelle. Les méthodes proposées sont finalement appliquées à la modélisation de données de rendements financiers. La revue canadienne de statistique 40: 86–109; 2012 © 2012 Société statistique du Canada" @default.
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- W2015220578 date "2012-02-14" @default.
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- W2015220578 title "Pair-copula constructions for non-Gaussian DAG models" @default.
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- W2015220578 doi "https://doi.org/10.1002/cjs.10131" @default.
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