Statistical inference on a changing extreme value dependence structure
研究了独立但非同分布的多变量正则变化随机向量的极值依赖结构，提出了谱测度的局部和积分估计量，并基于过程收敛构造了检验谱测度是否随时间变化的假设检验。
We analyze the extreme value dependence of independent, not necessarily identically distributed multivariate regularly varying random vectors. More specifically, we propose estimators of the spectral measure locally at some time point and of the spectral measures integrated over time. The uniform asymptotic normality of these estimators is proved under suitable nonparametric smoothness and regularity assumptions. We then use the process convergence of the integrated spectral measure to devise consistent tests for the null hypothesis that the spectral measure does not change over time.