Local robust estimation of the Pickands dependence function
Résumé
We consider the robust estimation of the Pickands dependence function in the covariate framework. Our estimator is based on local estimation with the minimum density power divergence criterion. We provide its 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 illustrated on a small simulation study involving both uncontaminated and contaminated samples.
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