Local Estimation of the Conditional Stable Tail Dependence Function
研究在存在随机协变量时，如何局部估计稳定尾部依赖函数，提出了加权经验估计量，并证明了其渐近性质，通过模拟和空气污染数据验证了效果。
Abstract We consider the local estimation of the stable tail dependence function when a random covariate is observed together with the variables of main interest. Our estimator is a weighted version of the empirical estimator adapted to the covariate framework. We provide the main asymptotic properties of our estimator, when properly normalized, in particular the convergence of the empirical process towards a tight centred Gaussian process. The finite sample performance of our estimator is illustrated on a small simulation study and on a dataset of air pollution measurements.