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Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Stichworte:
Sparsity, Copula, Graphical models
Dewey Dezimalklassifikation:
510 Mathematik
Zeitschriftentitel:
Computational Statistics & Data Analysis
Jahr:
2019
Band / Volume:
137
Jahr / Monat:
2019-09
Quartal:
3. Quartal
Monat:
Sep
Seitenangaben Beitrag:
211-232
Sprache:
en
Volltext / DOI:
doi:10.1016/j.csda.2019.02.007
WWW:
ScienceDirect
Verlag / Institution:
Elsevier BV
E-ISSN:
0167-9473
Publikationsdatum:
01.09.2019
TUM Einrichtung:
Professur für Angewandte Mathematische Statistik
Format:
Text
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