Estimating a directed tree for extremes
成果类型：
Article
署名作者：
Tran, Ngoc Mai; Buck, Johannes; Klueppelberg, Claudia
署名单位：
University of Texas System; University of Texas Austin; Technical University of Munich
刊物名称：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
ISSN/ISSBN：
1369-7412
DOI：
10.1093/jrsssb/qkad165
发表日期：
2024
页码：
771-792
关键词：
摘要：
We propose a new method to estimate a root-directed spanning tree from extreme data. Prominent example is a river network, to be discovered from extreme flow measured at a set of stations. Our new algorithm utilizes qualitative aspects of a max-linear Bayesian network, which has been designed for modelling causality in extremes. The algorithm estimates bivariate scores and returns a root-directed spanning tree. It performs extremely well on benchmark data and on new data. We prove that the new estimator is consistent under a max-linear Bayesian network model with noise. We also assess its strengths and limitations in a small simulation study.
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