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Titel:

Model distances for vine copulas in high dimensions

Dokumenttyp:
Zeitungsartikel
Autor(en):
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...     »
Stichworte:
Vine copulas, Model distances, Kullback–Leibler, Jeffreys distance, Monte Carlo integration
Dewey Dezimalklassifikation:
510 Mathematik
Zeitschriftentitel:
Statistics and Computing
Jahr:
2018
Band / Volume:
28
Jahr / Monat:
2018-03
Quartal:
1. Quartal
Monat:
Mar
Heft / Issue:
2
Seitenangaben Beitrag:
323–341
Reviewed:
ja
Sprache:
en
WWW:
Springer
Verlag / Institution:
Springer
Print-ISSN:
0960-3174
E-ISSN:
1573-1375
Status:
Erstveröffentlichung
Angenommen (von Zeitschrift):
30.01.2017
TUM Einrichtung:
Lehrstuhl für Mathematische Statistik
Format:
Text
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