Local robust estimation of the pickands dependence function

Mikael Escobar-Bach, Yuri Goegebeur, Armelle Guillou

Research output: Contribution to journalJournal articleResearchpeer-review


We consider the robust estimation of the Pickands dependence function in the random covariate framework. Our estimator is based on local estimation with the minimum density power divergence criterion. We provide the main asymptotic properties, in particular the convergence of the stochastic process, correctly normalized, towards a tight centered Gaussian process. The finite sample performance of our estimator is evaluated with a simulation study involving both uncontaminated and contaminated samples. The method is illustrated on a dataset of air pollution measurements.

Original languageEnglish
JournalAnnals of Statistics
Issue number6A
Pages (from-to)2806-2843
Publication statusPublished - 2018


  • Conditional pickands dependence function
  • Robustness
  • Stochastic convergence


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