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Dependent Lindeberg central limit theorem for the fidis of empirical processes of cluster functionals

机译:簇泛函经验过程的无穷依赖林德伯格中心极限定理

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摘要

Drees H. and Rootzen H. [Limit theorems for empirical processes of cluster functionals (EPCF). Ann Stat. 2010;38(4):2145-2186] have proven central limit theorems (CLTs) for EPCF built from beta-mixing processes. However, this family of beta-mixing processes is quite restrictive. We expand some of those results, for the finite-dimensional marginal distributions (fidis), to a more general dependent processes family, known as weakly dependent processes in the sense of Doukhan P. and Louhichi S. [A new weak dependence condition and applications to moment inequalities. Stoch. Proc. Appl. 1999;84:313-342]. In this context, the CLT for the fidis of EPCF is sufficient in some applications. For instance, we prove the convergence without mixing conditions of the extremogram estimator, including a small example with simulation of the extremogram of a weakly dependent random process but nonmixing, in order to confirm the efficacy of our result.
机译:Drees H.和Rootzen H. [关于集群功能(EPCF)经验过程的极限定理。安统计2010; 38(4):2145-2186]已证明由β混合过程构建的EPCF的中心极限定理(CLT)。但是,这一系列的beta混合过程非常严格。对于有限维边际分布(fidis),我们将其中一些结果扩展到更一般的依赖过程族,在Doukhan P.和Louhichi S的意义上称为弱依赖过程。[一种新的弱依赖条件和应用瞬间的不平等。斯托克程序应用1999; 84:313-342]。在这种情况下,EPCF领域的CLT在某些应用中就足够了。例如,我们证明了极值图估计量没有混合条件的收敛性,包括一个带有弱相关随机过程极值图模拟但没有混合的小例子,以确认我们的结果的有效性。

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