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Title:

Tail dependence of recursive max-linear models with regularly varying noise variables

Document type:
Zeitungsartikel
Author(s):
Gissibl, N., Klüppelberg, C., and Otto, M.
Abstract:
Recursive max-linear structural equation models with regularly varying noise variables are considered. Their causal structure is represented by a directed acyclic graph (DAG). The problem of identifying a recursive max-linear model and its associated DAG from its matrix of pairwise tail dependence coefficients is discussed. For example, it is shown that if a causal ordering of the associated DAG is additionally known, then the minimum DAG representing the recursive structural equations can be re...     »
Keywords:
Causal inference; Directed acyclic graph; Extreme value theory; Graphical model; Max-linear model; Max-stable model; Regular variation; Structural equation model; Tail dependence coefficient
Dewey Decimal Classification:
510 Mathematik
Journal title:
Econometrics and Statistics
Year:
2018
Journal volume:
6
Year / month:
2018-04
Quarter:
1. Quartal
Month:
Apr
Pages contribution:
149-167
Language:
en
Fulltext / DOI:
doi:/10.1016/j.ecosta.2018.02.003
WWW:
Econometrics and Statistics
Publisher:
Elsevier B.V.
Notes:
Available online
Status:
Postprint / reviewed
Submitted:
22.01.2017
Accepted:
14.02.2018
Date of publication:
15.03.2018
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
 BibTeX