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Title:

Bayesian Model Selection of Regular Vine Copulas

Document type:
Zeitungsartikel
Author(s):
Gruber, L. and Czado, D.
Abstract:
Regular vine copulas are a flexible class of dependence models, but Bayesian methodology for model selection and inference is not yet fully developed. We propose sparsity-inducing but otherwise non-informative priors, and present novel proposals to enable reversible jump Markov chain Monte Carlo posterior simulation for Bayesian model selection and inference. Our method is the first to jointly estimate the posterior distribution of all trees of a regular vine copula. This represents a substantia...     »
Keywords:
multivariate analysis, dependence modeling, copula modeling, vine copulas, Bayesian inference, posterior simulation, importance sampling, simulation studies, financial analysis, risk forecasting
Dewey Decimal Classification:
510 Mathematik
Journal title:
Bayesian Analysis
Year:
2018
Journal volume:
13
Year / month:
2018-01
Quarter:
1. Quartal
Month:
Jan
Journal issue:
4
Pages contribution:
1111-1135
Fulltext / DOI:
doi:10.1214/17-BA1089
Publisher:
Int Soc Bayesian Analysis
Notes:
Published online
Date of publication:
12.01.2018
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
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