通过随机检验统计推断

IF 1.5 Q2 PHYSICS, MATHEMATICAL
Nikita Puchkin, V. Ulyanov
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引用次数: 3

摘要

我们表明,在特定情况下,外部随机化可能会强制检验统计量收敛到它们的极限分布。这导致了一个更清晰的推断。我们的方法是基于加权和的中心极限定理。我们将我们的方法应用于一类基于秩的检验统计量和一类散度检验统计量,并证明了随机统计量在相对于外部随机化的压倒性概率下,以$O(1/n)$的速率收敛于Kolmogorov度量中的极限卡方分布(直到一些对数因子)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inference via randomized test statistics
We show that external randomization may enforce the convergence of test statistics to their limiting distributions in particular cases. This results in a sharper inference. Our approach is based on a central limit theorem for weighted sums. We apply our method to a family of rank-based test statistics and a family of phi-divergence test statistics and prove that, with overwhelming probability with respect to the external randomization, the randomized statistics converge at the rate $O(1/n)$ (up to some logarithmic factors) to the limiting chi-square distribution in Kolmogorov metric.
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来源期刊
CiteScore
2.30
自引率
0.00%
发文量
16
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