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Title:

Dependence Modeling in Ultra High Dimensions with Vine Copulas and the Graphical Lasso

Document type:
Zeitungsartikel
Author(s):
Müller, D. and Czado C.
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...     »
Keywords:
Dependence Modeling, Graphical Lasso, Copulas, Regular Vines, Clustering
Journal title:
Preprint
Year:
2017
Reviewed:
ja
Language:
en
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
Ingested:
15.09.2017
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