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Document type:
Zeitschriftenaufsatz 
Author(s):
Müller, D. and Czado, C. 
Title:
Dependence modelling 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 proposed approach overcomes this burden and makes the first step into ultra high dimensional non-Gaussian...    »
 
Keywords:
Sparsity, Copula, Graphical models 
Dewey Decimal Classification:
510 Mathematik 
Journal title:
Computational Statistics & Data Analysis 
Year:
2019 
Journal volume:
137 
Year / month:
2019-09 
Quarter:
3. Quartal 
Month:
Sep 
Pages contribution:
211-232 
Language:
en 
Publisher:
Elsevier BV 
E-ISSN:
0167-9473 
Date of publication:
01.09.2019 
TUM Institution:
Professur für Angewandte Mathematische Statistik 
Format:
Text