mlsanityData to 7 Oct 2026

Estimating a Directed Tree for Extremes

arXiv 2102.06197 ↗First posted 11 Feb 2021stat.ML cs.LG stat.APcited by 5

Not on the map. Why?

Abstract (arXiv)

We propose a new method to estimate a root-directed spanning tree from extreme data. A 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 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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 10 of its 70 references that are in the map. First posted on arXiv 11 Feb 2021.

Builds on

10 of 70 references.

References · 10

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  2. 2016Structure Learning in Graphical Modelingcited by 66
  3. 2019Causal discovery in heavy-tailed modelscited by 18
  4. 2015Extremes on river networkscited by 9
  5. 2020Structure learning for extremal tree modelscited by 6
  6. 2016A continuous updating weighted least squares estimator of tail dependence in high dimensionscited by 3
  7. 2017Tail dependence of recursive max-linear models with regularly varying noise variablescited by 3
  8. 2019Bayesian Networks for Max-linear Modelscited by 3
  9. 2019Estimating an Extreme Bayesian Network via Scalingscited by 2
  10. 2017Open storm: a complete framework for sensing and control of urban watershedscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.

  1. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 17
  2. 2025Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discoverycited by 7
  3. 2024Sortability of Time Series Datacited by 3
  4. 2025Inference for max-linear Bayesian networks with noisecited by 1
  5. 2026Extrapolation in Statistical Learning with Extreme Value Theorycited by 1