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Title:

Identifiability and estimation of recursive max-linear models

Document type:
Zeitungsartikel
Author(s):
Gissibl, N., Klüppelberg, C. and Lauritzen, S.
Abstract:
We address the identifiability and estimation of recursive max-linear structural equation models represented by an edge weighted directed acyclic graph (DAG). Such models are generally unidentifiable and we identify the whole class of DAGs and edge weights corresponding to a given observational distribution. For estimation, standard likelihood theory cannot be applied because the corresponding families of distributions are not dominated. Given the underlying DAG, we present an estimator for the...     »
Keywords:
Causal inference, Bayesian network, directed acyclic graph, extreme value theory, generalized maximum likelihood estimation, graphical model, identifiability, max-linear model, structural equation model.
Dewey Decimal Classification:
510 Mathematik
Journal title:
Scandinavian Journal of Statistics
Year:
2021
Journal volume:
48
Year / month:
2021-03
Quarter:
1. Quartal
Month:
Mar
Journal issue:
1
Pages contribution:
188-211
Language:
en
Fulltext / DOI:
doi:10.1111/sjos.12446
Publisher:
Wiley
Notes:
online Veröffentlichung: 28. April 2020
Status:
Erstveröffentlichung
Date of publication:
01.03.2021
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
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