Some Theory of Statistical Inference for Nonlinear Science
展示了如何估计相关维数、Kolmogorov熵代理指标和不稳定性度量等非线性科学指标的标准误，主要使用混合过程的U统计量中心极限理论，为非线性科学和混沌理论中的正式假设检验迈出一步。
This article shows how standard errors can be estimated for a measure of the number of excited degrees of freedom (the correlation dimension), and a measure of the rate of information creation (a proxy for the Kolmogorov entropy), and a measure of instability. These measures are motivated by nonlinear science and chaos theory. The main analytical method is central limit theory of U-statistics for mixing processes. The paper takes a step toward formal hypothesis testing in nonlinear science and chaos theory.