论文标题
讨论多变量依赖的多尺度Fisher独立测试
Discussion of Multiscale Fisher's Independence Test for Multivariate Dependence
论文作者
论文摘要
Gorsky＆Ma（2022）提出的多尺度Fisher的独立测试（以下文章）是一种测试两个随机矢量之间独立性的新方法。通过其设计，该测试在检测局部依赖性方面特别有用。此外，通过采用无重新采样方法，它可以轻松适应大量样本量。该方法的另一个好处是它可以解释依赖性的性质。我们祝贺作者Shai Gorksy和Li Ma，他们非常有趣和优雅的作品。在此评论中，我们想讨论一个统一多项测试和其他测试的一般框架，并将其与Lee等人提出的二进制扩展随机集合测试（贝雷帽）进行比较。 （在印刷中）。我们还想对该方法的潜在扩展贡献我们的想法。
The multiscale Fisher's independence test (MULTIFIT hereafter) proposed by Gorsky & Ma (2022) is a novel method to test independence between two random vectors. By its design, this test is particularly useful in detecting local dependence. Moreover, by adopting a resampling-free approach, it can easily accommodate massive sample sizes. Another benefit of the proposed method is its ability to interpret the nature of dependency. We congratulate the authors, Shai Gorksy and Li Ma, for their very interesting and elegant work. In this comment, we would like to discuss a general framework unifying the MULTIFIT and other tests and compare it with the binary expansion randomized ensemble test (BERET hereafter) proposed by Lee et al. (In press). We also would like to contribute our thoughts on potential extensions of the method.
