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Document type:
Zeitungsartikel 
Author(s):
Nagler, T. and Czado, C. 
Title:
Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas 
Abstract:
Practical applications of nonparametric density estimators in more than three dimensions suffer a great deal from the well-known curse of dimensionality: convergence slows down as dimension increases. We show that one can evade the curse of dimensionality by assuming a simplified vine copula model for the dependence between variables. We formulate a general nonparametric estimator for such a model and show under high-level assumptions that the speed of convergence is independent of dimension. We...    »
 
Keywords:
Asymptotic; Classification; Copula; Dependence; Kernel density; Vine 
Dewey Decimal Classification:
510 Mathematik 
Journal title:
Journal of Multivariate Analysis 
Year:
2016 
Journal volume:
151 
Year / month:
2016-10 
Quarter:
4. Quartal 
Month:
Oct 
Journal issue:
Pages contribution:
69-89 
Reviewed:
ja 
Language:
en 
Publisher:
Academic Press, Inc. 
Publisher address:
Orlando, FL, USA 
Status:
Verlagsversion / published 
TUM Institution:
Lehrstuhl für Mathematische Statistik 
Format:
Text 
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