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Title:

Pair-copula Bayesian networks

Document type:
Zeitungsartikel
Author(s):
Bauer, A. and Czado, C.
Abstract:
Pair-copula Bayesian networks (PCBNs) are a novel class of multivariate statistical models, which combine the distributional flexibility of pair-copula constructions (PCCs) with the parsimony of conditional independence models associated with directed acyclic graphs (DAGs). We are first to provide generic algorithms for random sampling and likelihood inference in arbitrary PCBNs as well as for selecting orderings of the parents of the vertices in the underlying graphs. Model selection of the DAG...     »
Keywords:
Conditional independence test, Copulas, Directed acyclic graphs, Graphical models, PC algorithm, Regular vines
Dewey Decimal Classification:
510 Mathematik
Journal title:
Journal of Computational and Graphical Statistics
Year:
2016
Journal volume:
25
Year / month:
2016-11
Quarter:
4. Quartal
Month:
Nov
Journal issue:
4
Pages contribution:
1248–1271
Language:
en
Fulltext / DOI:
doi:10.1080/10618600.2015.1086355
Publisher:
Taylor & Francis Group
Notes:
published online
Status:
Verlagsversion / published
Date of publication:
10.11.2016
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
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