Local robust estimation of the Pickands dependence function
提出一种在随机协变量框架下稳健估计Pickands依赖函数的方法，基于最小密度功率散度准则进行局部估计，并给出渐近性质与模拟验证。
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.