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Title:

Estimating an Extreme Bayesian Network via Scalings

Document type:
Zeitungsartikel
Author(s):
Klüppelberg, C.; Krali, M.
Abstract:
Recursive max-linear vectors model causal dependence between its components by expressing each node variable as a max-linear function of its parental nodes in a directed acyclic graph and some exogenous innovation. Motivated by extreme value theory, innovations are assumed to have regularly varying distribution tails. We propose a scaling technique in order to determine a causal order of the node variables. All dependence parameters are then estimated from the estimated scalings. Furthermore, we...     »
Keywords:
causal order, directed acyclic graph, extreme value statistics, graphical model, recursive max-linear model, regular variation, structural equation model, structure learning
Dewey Decimal Classification:
510 Mathematik
Journal title:
Journal of Multivariate Analysis
Year:
2021
Journal volume:
181
Year / month:
2021-01
Quarter:
1. Quartal
Month:
Jan
Pages contribution:
104672
Language:
en
Fulltext / DOI:
doi:10.1016/j.jmva.2020.104672
WWW:
arxiv.org
Publisher:
Elsevier
E-ISSN:
0047-259X
Status:
Erstveröffentlichung
Submitted:
09.12.2019
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
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