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Title:

Estimating a Latent Tree for Extremes

Document type:
Zeitungsartikel
Author(s):
Tran, Ngoc Mai; Buck, Johannes; Klüppelberg, Claudia
Abstract:
The Latent River Problem has emerged as a flagship problem for causal discovery in extreme value statistics. This paper gives QTree, a simple and efficient algorithm to solve the Latent River Problem that outperforms existing methods. QTree returns a directed graph and achieves almost perfect recovery on the Upper Danube, the existing benchmark dataset, as well as on new data from the Lower Colorado River in Texas. It can handle missing data, has an automated parameter tuning procedure, and runs...     »
Keywords:
causal inference, max-linear, Bayesian networks, extreme values statistics, directed graphical models
Dewey Decimal Classification:
510 Mathematik
Journal title:
Preprint
Year:
2021
Language:
en
WWW:
Arxiv
Status:
Preprint / submitted
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
Ingested:
23.08.2021
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