Abstract We consider robust and nonparametric estimation of the coefficient of tail dependence in presence of random covariates. The estimator is obtained by fitting the extended Pareto distribution locally to properly transformed bivariate observations using the minimum density power divergence criterion. We establish convergence in probability and asymptotic normality of the proposed estimator under some regularity conditions. The finite sample performance is evaluated with a small simulation experiment, and the practical applicability of the method is illustrated on a real dataset of air pollution measurements.

中文翻译：

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条件尾相关系数的鲁棒非参数估计

摘要 我们考虑在存在随机协变量的情况下对尾部依赖系数的鲁棒性和非参数估计。估计量是通过使用最小密度功率发散标准将扩展的帕累托分布局部拟合到适当变换的双变量观测值来获得的。我们在某些规律性条件下建立了所提出估计量的概率和渐近正态性的收敛。通过小型模拟实验评估有限样本性能，并在空气污染测量的真实数据集上说明了该方法的实际适用性。