.png){kind=icon}
.png){kind=icon}
Return
Estimating a directed tree for extremes
.png){kind=icon}
OA
AI
DOI：10.1093/jrsssb/qkad165.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Bayesian network
causal inference
directed acyclic graph
extreme value analysis
graphical model
max-linear model
Journal
J
IF:
3.6
Papers:
1.5K
Citations:
3.2W