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Dokumenttyp:
Zeitungsartikel 
Autor(en):
Müller, D. and Czado C. 
Titel:
Dependence Modeling in Ultra High Dimensions with Vine Copulas and the Graphical Lasso 
Abstract:
To model high dimensional data, Gaussian methods are widely used since they remain tractable and yield parsimonious models by imposing strong assumptions on the data. Vine copulas are more flexible by combining arbitrary marginal distributions and (conditional) bivariate copulas. Yet, this adaptability is accompanied by sharply increasing computational effort as the dimension increases. The approach proposed in this paper overcomes this burden and makes the first step into ultra high dimensional...    »
 
Stichworte:
Dependence Modeling, Graphical Lasso, Copulas, Regular Vines, Clustering 
Zeitschriftentitel:
Preprint 
Jahr:
2017 
Reviewed:
ja 
Sprache:
en 
WWW:
_blank 
TUM Einrichtung:
Lehrstuhl für Mathematische Statistik 
Format:
Text