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The LLN and CLT for U-statistics under cross-sectional dependence
Journal of Nonparametric Statistics ( IF 0.8 ) Pub Date : 2020-01-02 , DOI: 10.1080/10485252.2019.1711378
Yiguo Sun 1
Affiliation  

In this paper we establish the law of large numbers (LLN) and central limit theorem (CLT) for second-order kernel-weighted U-statistics of cross-sectionally dependent variables. To illustrate the usefulness of our theorems, we apply the new LLN and CLT to nonparametric model misspecification testing in spatial regression framework. Monte Carlo simulations are used to assess the finite sample performance of our test statistic.

中文翻译:

横截面依赖下 U 统计量的 LLN 和 CLT

在本文中,我们为横截面因变量的二阶核加权 U 统计量建立了大数定律 (LLN) 和中心极限定理 (CLT)。为了说明我们的定理的有用性,我们将新的 LLN 和 CLT 应用于空间回归框架中的非参数模型错误指定测试。Monte Carlo 模拟用于评估我们的测试统计量的有限样本性能。
更新日期:2020-01-02
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