论文标题
通过加权局部和最近的邻居进行有条件的独立测试
Conditional independence testing via weighted partial copulas and nearest neighbors
论文作者
论文摘要
本文介绍了用于测试条件独立性的\ textit {加权部分copula}功能。提出的测试程序来自这两种成分：（i）测试统计量是对\ textIt {加权部分copula}的明确的cramer-von mises转换，（ii）使用bootstrap过程计算拒绝区域，该区域是通过bootstrap程序来模拟估计条件的估计条件测量的样本来模拟样品的条件独立性的。在条件独立性下，当使用平滑的局部线性估计器估算边际估算时，建立了\ textIt {加权部分copula proces} s的弱收敛性。最后，一个实验部分表明，与最新的最新方法（例如基于内核测试）相比，所提出的测试具有竞争力。
This paper introduces the \textit{weighted partial copula} function for testing conditional independence. The proposed test procedure results from these two ingredients: (i) the test statistic is an explicit Cramer-von Mises transformation of the \textit{weighted partial copula}, (ii) the regions of rejection are computed using a bootstrap procedure which mimics conditional independence by generating samples from the product measure of the estimated conditional marginals. Under conditional independence, the weak convergence of the \textit{weighted partial copula proces}s is established when the marginals are estimated using a smoothed local linear estimator. Finally, an experimental section demonstrates that the proposed test has competitive power compared to recent state-of-the-art methods such as kernel-based test.
