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Title:

Dependence modelling in ultra high dimensions with vine copulas and the Graphical Lasso

Document type:
Zeitschriftenaufsatz
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 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
Fulltext / DOI:
doi:10.1016/j.csda.2019.02.007
WWW:
ScienceDirect
Publisher:
Elsevier BV
E-ISSN:
0167-9473
Date of publication:
01.09.2019
TUM Institution:
Professur für Angewandte Mathematische Statistik
Format:
Text
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