论文标题
FASTMI：基于快速，基于Copula的互助估计器
fastMI: a fast and consistent copula-based estimator of mutual information
论文作者
论文摘要
作为信息理论中的基本概念，相互信息（$ mi $）通常用于量化随机向量之间的关联。 $ mi $的大多数现有非参数估计器具有不稳定的统计性能，因为它们涉及参数调整。我们开发了一个一致且强大的估计器，称为FastMI，不会引起任何参数调整。基于Copula公式，FastMI通过利用基于快速傅立叶变换的基础密度估计来估计$ mi $。广泛的仿真研究表明，FastMI的表现优于最先进的估计器，具有提高的估计精度和大型数据集的运行时间。 FastMI为独立性提供了强大的测试，表现出令人满意的I型错误控制。预计这将是在广泛数据中估算共同信息的强大工具，我们为更广泛的传播开发了R软件包FastMI。
As a fundamental concept in information theory, mutual information ($MI$) has been commonly applied to quantify association between random vectors. Most existing nonparametric estimators of $MI$ have unstable statistical performance since they involve parameter tuning. We develop a consistent and powerful estimator, called fastMI, that does not incur any parameter tuning. Based on a copula formulation, fastMI estimates $MI$ by leveraging Fast Fourier transform-based estimation of the underlying density. Extensive simulation studies reveal that fastMI outperforms state-of-the-art estimators with improved estimation accuracy and reduced run time for large data sets. fastMI provides a powerful test for independence that exhibits satisfactory type I error control. Anticipating that it will be a powerful tool in estimating mutual information in a broad range of data, we develop an R package fastMI for broader dissemination.
