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Title:

Model distances for vine copulas in high dimensions

Document type:
Zeitungsartikel
Author(s):
Killiches, M., Kraus, D., and Czado, C.
Abstract:
Vine copulas are a flexible class of dependence models consisting of bivariate building blocks and have proven to be particularly useful in high dimensions. Classical model distance measures require multivariate integration and thus suffer from the curse of dimensionality. In this paper, we provide numerically tractable methods to measure the distance between two vine copulas even in high dimensions. For this purpose, we consecutively develop three new distance measures based on the Kullback–Lei...     »
Keywords:
Vine copulas, Model distances, Kullback–Leibler, Jeffreys distance, Monte Carlo integration
Dewey Decimal Classification:
510 Mathematik
Journal title:
Statistics and Computing
Year:
2018
Journal volume:
28
Year / month:
2018-03
Quarter:
1. Quartal
Month:
Mar
Journal issue:
2
Pages contribution:
323–341
Reviewed:
ja
Language:
en
WWW:
Springer
Publisher:
Springer
Print-ISSN:
0960-3174
E-ISSN:
1573-1375
Status:
Erstveröffentlichung
Accepted:
30.01.2017
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
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