最近邻下离散和连续混合变量的条件互信息估计
Conditional Mutual Information Estimation for Mixed Discrete and Continuous Variables with Nearest Neighbors
摘要：
公共卫生、公共政策和社会科学等领域通常希望量化变量之间的依赖程度，这些变量之间的关系采用未知的函数形式。事实上，这些领域的研究人员通常试图评估因果理论，因此希望在对其他可能解释、调解或混淆因果关系的变量进行调节后，量化依赖性。条件互信息没有更广泛地用于这些任务的一个原因是缺乏可以处理连续和离散随机变量的组合的估计器，这在应用中是常见的。本文提出了一种新的方法来估计互信息和条件互信息的数据样本包含离散和连续变量的混合。我们证明了这种估计是一致的，并通过模拟显示，它是更准确的比类似的估计。
英文摘要：
Fields like public health, public policy, and social science often want to quantify the degree of dependence between variables whose relationships take on unknown functional forms. Typically, in fact, researchers in these fields are attempting to evaluate causal theories, and so want to quantify dependence after conditioning on other variables that might explain, mediate or confound causal relations. One reason conditional mutual information is not more widely used for these tasks is the lack of estimators which can handle combinations of continuous and discrete random variables, common in applications. This paper develops a new method for estimating mutual and conditional mutual information for data samples containing a mix of discrete and continuous variables. We prove that this estimator is consistent and show, via simulation, that it is more accurate than similar estimators.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
