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. The finite sample performance of these tests is investigated in Monte Carlo simulations.

翻译：本文分析了独立（不必同分布）多元规则变化随机向量的极值依赖结构。具体而言，我们提出了某时间点局部谱测度及时间积分谱测度的估计量，并在合适的非参数光滑性与正则性假设下证明了这些估计量的一致渐近正态性。进一步，利用积分谱测度的过程收敛性，我们构造了检验谱测度随时间不变原假设的一致性检验。蒙特卡洛模拟评估了这些检验在有限样本下的表现。