Benutzer: Gast  Login
Titel:

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

Dokumenttyp:
Zeitungsartikel
Autor(en):
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...     »
Stichworte:
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 Dezimalklassifikation:
510 Mathematik
Zeitschriftentitel:
Econometrics and Statistics
Jahr:
2018
Band / Volume:
6
Jahr / Monat:
2018-04
Quartal:
1. Quartal
Monat:
Apr
Seitenangaben Beitrag:
149-167
Sprache:
en
Volltext / DOI:
doi:/10.1016/j.ecosta.2018.02.003
WWW:
Econometrics and Statistics
Verlag / Institution:
Elsevier B.V.
Hinweise:
Available online
Status:
Postprint / reviewed
Eingereicht (bei Zeitschrift):
22.01.2017
Angenommen (von Zeitschrift):
14.02.2018
Publikationsdatum:
15.03.2018
TUM Einrichtung:
Lehrstuhl für Mathematische Statistik
Format:
Text
 BibTeX