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Bootstrapped Deattenuated Correlation: Nonnormal Distributions

机译：自举衰减相关：非正态分布

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摘要

Correlation attenuation due to measurement error and a corresponding correction, the deattenuated correlation, have been known for over a century. Nevertheless, the deattenuated correlation remains underutilized. A few studies in recent years have investigated factors affecting the deattenuated correlation, and a couple of them provide alternative solutions based on the deattenuated correlation. One study proposed bootstrap confidence intervals (CIs) for the deattenuated correlation. However, CI research for the deattenuated correlation is in the beginning phases. Therefore, the bootstrapped deattenuated correlation CIs are investigated for 95% coverage through a Monte Carlo simulation that includes nonnormal distributions. Overall, both the bias-corrected and accelerated (BCa) and percentile bootstrap (PB) CIs had good performance, but the BCa CIs had slightly better coverage. In addition, with the exception of the Pareto distribution, both CIs had good coverage under all simulation conditions and across all other investigated distributions (i.e., the Normal, Uniform, Triangular, Beta, and Laplace).
机译：由于测量误差引起的相关衰减和相应的校正，即衰减的相关，已经有一个多世纪的历史了。但是，衰减后的相关性仍未得到充分利用。近年来，一些研究已经研究了影响衰减相关的因素，其中有一些提供了基于衰减相关的替代解决方案。一项研究提出了用于衰减相关的自举置信区间（CIs）。但是，针对衰减相关的CI研究仍处于起步阶段。因此，通过包括非正态分布的蒙特卡洛模拟研究了自举后的衰减后的相关CI的95％覆盖率。总体而言，偏差校正和加速（BCa）以及百分位数自举（PB）CI均具有良好的性能，但BCa CI的覆盖范围稍好。此外，除了帕累托分布外，两个配置项在所有模拟条件下以及所有其他调查分布（即正态分布，均匀分布，三角分布，Beta和拉普拉斯分布）均具有良好的覆盖率。

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