On extreme quantile region estimation under heavy-tailed elliptical distributions
提出一种仿射等变的极端分位数区域估计方法，适用于重尾椭圆分布，在估计位置和散度下证明一致性，并通过模拟和实例验证实用性。
Consider the estimation of an extreme quantile region corresponding to a very small probability. Estimation of extreme quantile regions is important but difficult since extreme regions contain only a few or no observations. In this article, we propose an affine equivariant extreme quantile region estimator for heavy-tailed elliptical distributions. The estimator is constructed by extending a well-known univariate extreme quantile estimator. Consistency of the estimator is proved under estimated location and scatter. The practicality of the developed estimator is illustrated with simulations and a real data example.