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Document type:
Zeitungsartikel 
Author(s):
Gissibl, N., Klüppelberg, C., and Otto, M. 
Title:
Tail dependence of recursive max-linear models with regularly varying noise variables 
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:
Year / month:
2018-04 
Quarter:
1. Quartal 
Month:
Apr 
Pages contribution:
149-167 
Language:
en 
Publisher:
Elsevier B.V. 
Notes:
Available online 
Status:
Postprint / reviewed 
Accepted:
14.02.2018 
Date of publication:
15.03.2018 
TUM Institution:
Lehrstuhl für Mathematische Statistik 
Format:
Text