论文标题
从定期变化的随机向量的凸孔估计光谱度量的估计
Estimation of the Spectral Measure from ConvexCombinations of Regularly Varying RandomVectors
论文作者
论文摘要
定期变化的随机矢量XI的极端依赖性结构通过其极限光谱度量充分描述。在本文中，我们研究了该度量的Torecover特征（例如极端系数）如何从X组成部分的凸组合的极端组合中。我们的考虑导致光谱室相应组合的新估计量的aclass。我们通过功能极限定理显示渐近正态性，并以极端系数的估计为重点，我们验证使用插入式估算器使用次采样引导程序可以实现最小的渐近变量。我们说明了在模拟和真实数据上的方法的好处。
The extremal dependence structure of a regularly varying random vector Xis fully described by its limiting spectral measure. In this paper, we investigate how torecover characteristics of the measure, such as extremal coefficients, from the extremalbehaviour of convex combinations of components of X. Our considerations result in aclass of new estimators of moments of the corresponding combinations for the spectralvector. We show asymptotic normality by means of a functional limit theorem and, focusingon the estimation of extremal coefficients, we verify that the minimal asymptoticvariance can be achieved by a plug-in estimator using subsampling bootstrap. We illustratethe benefits of our approach on simulated and real data.
