User: Guest  Login
More Searchfields
Simple search
Original title:
Causal Inference of Extremes
Original subtitle:
Recursive Max-Linear Models Under Observational Noise
Translated title:
Kausale Inferenz von Extremen
Translated subtitle:
Rekursive Max-Lineare Modelle unter Beobachtungsrauschen
Author:
Buck, Johannes Ernst-Emanuel
Year:
2023
Document type:
Dissertation
Faculty/School:
TUM School of Computation, Information and Technology
Advisor:
Klüppelberg, Claudia (Prof. Dr.)
Referee:
Klüppelberg, Claudia (Prof. Dr.); Améndola Cerón, Carlos Enrique (Prof. Dr.)
Language:
en
Subject group:
MAT Mathematik
Keywords:
causal inference, directed acyclic graph, extreme value theory, graphical model, max-linear model, structural equation model, machine learning, identifiability
Translated keywords:
kausale Inferenz, gerichteter azyklischer Graph, Extremwerttheorie, grafisches Modell, max-lineares Modell, Strukturgleichungsmodell, machine learning, Identifizierbarkeit
TUM classification:
MAT 620
Abstract:
Recursive max-linear models have gained increasing interest for modeling extremal dependence. The causal structure of such models is represented by a Bayesian network, where node variables are defined as a max-linear function of parental node variables and an independent innovation. In this dissertation, we study recursive max-linear models under observational noise. Particularly, we investigate structural properties and causal inference of noisy max-linear models.
Translated abstract:
Rekursive max-lineare Modelle haben zunehmendes Interesse für die Modellierung extremaler Abhängigkeiten erlangt. Die kausale Struktur solcher Modelle wird durch eine max-lineare Funktion von übergeordneten Knotenvariablen und einer unabhängigen Innovation definiert. In dieser Dissertation untersuchen wir rekursive max-lineare Modelle unter Beobachtungsrauschen. Insbesondere betrachten wir strukturelle Eigenschaften und kausale Inferenz von verrauschten max-linearen Modellen
WWW:
https://mediatum.ub.tum.de/?id=1695755
Date of submission:
24.01.2023
Oral examination:
22.03.2023
File size:
7745418 bytes
Pages:
146
Urn (citeable URL):
https://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:91-diss-20230322-1695755-1-3
Last change:
14.04.2023
 BibTeX