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- 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
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