论文标题
不对称依赖测量和测试
Asymmetric Dependence Measurement and Testing
论文作者
论文摘要
测量两个变量（事件）（XI和XJ）之间的（因果）方向和强度对于所有科学都是基础。我们对数十年的统计依赖性文献的调查表明，大多数人从某种意义上说，XI对XJ的依赖性的强度正好等于XJ对XI的依赖强度。但是，我们表明，在许多现实世界中，这种对称性通常是不正确的，既不必要也不足够。 Vinod（2014）在普遍相关系数的[-1，1]中的不对称矩阵r*提供了直觉上吸引人的，易于解释的依赖性措施。本文提出了使用Taraldsen（2021）相关系数和引导程序的精确采样分布对R*的统计推断。当已知方向时，提出的不对称（一尾）测试具有更大的功率。
Measuring the (causal) direction and strength of dependence between two variables (events), Xi and Xj , is fundamental for all science. Our survey of decades-long literature on statistical dependence reveals that most assume symmetry in the sense that the strength of dependence of Xi on Xj exactly equals the strength of dependence of Xj on Xi. However, we show that such symmetry is often untrue in many real-world examples, being neither necessary nor sufficient. Vinod's (2014) asymmetric matrix R* in [-1, 1] of generalized correlation coefficients provides intuitively appealing, readily interpretable, and superior measures of dependence. This paper proposes statistical inference for R* using Taraldsen's (2021) exact sampling distribution of correlation coefficients and the bootstrap. When the direction is known, proposed asymmetric (one-tail) tests have greater power.
