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Continuous mapping approach to the asymptotics of U- and V-statistics

机译:连续映射方法渐近于U统计量和V统计量

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摘要

We derive a new representation for U- and V-statistics. Using this representation, the asymptotic distribution of U- and V-statistics can be derived by a direct application of the Continuous Mapping theorem. That novel approach not only encompasses most of the results on the asymptotic distribution known in literature, but also allows for the first time a unifying treatment of non-degenerate and degenerate U- and V-statistics. Moreover, it yields a new and powerful tool to derive the asymptotic distribution of very general U- and V-statistics based on long-memory sequences. This will be exemplified by several astonishing examples. In particular, we shall present examples where weak convergence of U- or V-statistics occurs at the rate a_n~3 and a_n~4, respectively, when a_n is the rate of weak convergence of the empirical process. We also introduce the notion of asymptotic (non-) degeneracy which often appears in the presence of long-memory sequences.
机译:我们得出U统计量和V统计量的新表示形式。使用这种表示,可以通过直接应用连续映射定理来推导U统计量和V统计量的渐近分布。这种新颖的方法不仅涵盖了文献中关于渐进分布的大多数结果,而且首次允许对非简并和简并的U统计量和V统计量进行统一处理。此外,它提供了一个强大的新工具,可基于长内存序列推导非常一般的U统计量和V统计量的渐近分布。这将通过几个惊人的例子来说明。特别是,当a_n是经验过程的弱收敛率时,我们将给出一些示例,其中U统计量或V统计量的弱收敛率分别以a_n〜3和a_n〜4的速率发生。我们还介绍了渐近(非)简并性的概念,这种现象经常出现在长内存序列的存在下。

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