Estimating an extreme Bayesian network via scalings

@article{Klppelberg2021EstimatingAE,
  title={Estimating an extreme Bayesian network via scalings},
  author={Claudia Kl{\"u}ppelberg and Mario Krali},
  journal={J. Multivar. Anal.},
  year={2021},
  volume={181},
  pages={104672}
}
Recursive max-linear vectors model causal dependence between its components by expressing each node variable as a max-linear function of its parental nodes in a directed acyclic graph and some exogenous innovation. Motivated by extreme value theory, innovations are assumed to have regularly varying distribution tails. We propose a scaling technique in order to determine a causal order of the node variables. All dependence parameters are then estimated from the estimated scalings. Furthermore… 

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