Independence tests in the presence of measurement errors: An invariance law

Independence tests in the presence of measurement errors: An invariance law
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存在测量误差时的独立性检验:不变律

DOI:
10.1016/j.jmva.2021.104818
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发表时间:
2021-09
影响因子:
1.6
通讯作者:
Zhu Liping
Zhu Liping
中科院分区:
数学2区
文献类型:
--
作者:
Fan Jinlin;Zhang Yaowu;Zhu Liping

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在许多科学领域,观测结果的收集带有测量误差。我们感兴趣的是测量和测试受测量误差影响的随机向量之间的独立性。我们对经典距离协方差中的权函数进行了修改,使主要感兴趣的随机向量之间的修改距离协方差与代理随机向量之间的经典距离协方差相同,这在本文中被称为不变性定律。测量误差的存在可能会大大削弱非线性依赖的程度。一个直接的问题出现了:代理向量之间的经典距离相关性不能达到1,即使两个主要感兴趣的随机向量是完全线性相关的。为了解决这个问题,我们建议使用重复测量来估计距离方差。我们深入研究了修正距离相关的渐近性质。此外,我们通过广泛的模拟和实际应用证明了它的有限样本性能。
In many scientific areas the observations are collected with measurement errors. We are interested in measuring and testing independence between random vectors which are subject to measurement errors. We modify the weight functions in the classic distance covariance such that, the modified distance covariance between the random vectors of primary interest is the same as its classic version between the surrogate random vectors, which is referred to as the invariance law in the present context. The presence of measurement errors may substantially weaken the degree of nonlinear dependence. An immediate issue arises: The classic distance correlation between the surrogate vectors cannot reach one even if the two random vectors of primary interest are exactly linearly dependent. To address this issue, we propose to estimate the distance variance using repeated measurements. We study the asymptotic properties of the modified distance correlation thoroughly. In addition, we demonstrate its finite-sample performance through extensive simulations and a real-world application.
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