论文标题
定期改变线性过程及其扩展的尾部对抗稳定性
Tail Adversarial Stability for Regularly Varying Linear Processes and their Extensions
论文作者
论文摘要
事实证明，尾部对抗稳定性的概念可用于获得有关尾部依赖时间序列的限制定理。在最大线性过程中已经检查了其对经典强混合框架的影响和优势，但尚未研究添加剂线性过程。在本文中，我们通过验证定期变化的添加线性过程的尾部对抗稳定性条件来填补这一空白。另外，我们将结果扩展到随机波动性概括和最大线性对应物。我们还解决了单调变换下的尾部对抗稳定性的不变性。还讨论了统计上下文中限制定理的一些含义。
The notion of tail adversarial stability has been proven useful in obtaining limit theorems for tail dependent time series. Its implication and advantage over the classical strong mixing framework has been examined for max-linear processes, but not yet studied for additive linear processes. In this article, we fill this gap by verifying the tail adversarial stability condition for regularly varying additive linear processes. We in addition consider extensions of the result to a stochastic volatility generalization and to a max-linear counterpart. We also address the invariance of tail adversarial stability under monotone transforms. Some implications for limit theorems in statistical context are also discussed.
