标题： 条件独立性检验与单个多元非平稳非线性时间序列实例 显示英文标题

标题： Conditional independence testing with a single realization of a multivariate nonstationary nonlinear time series

摘要： 识别随机过程之间的关系是处理复杂时间系统的学科（如经济学）的关键目标。尽管多元时间序列分析的标准工具箱有许多优势，但使用线性向量自回归模型很难捕捉非线性动态。 这种困难促使了变量选择、因果发现和非线性时间序列图形建模方法的发展，这些方法通常采用条件独立性的非参数检验。 在本文中，我们介绍了首个适用于单一实现的非平稳非线性过程的条件独立性检验框架。 关键技术要素包括时变非线性回归、时变协方差估计以及分布均匀强高斯近似。 显示英文摘要

摘要： Identifying relationships among stochastic processes is a key goal in disciplines that deal with complex temporal systems, such as economics. While the standard toolkit for multivariate time series analysis has many advantages, it can be difficult to capture nonlinear dynamics using linear vector autoregressive models. This difficulty has motivated the development of methods for variable selection, causal discovery, and graphical modeling for nonlinear time series, which routinely employ nonparametric tests for conditional independence. In this paper, we introduce the first framework for conditional independence testing that works with a single realization of a nonstationary nonlinear process. The key technical ingredients are time-varying nonlinear regression, time-varying covariance estimation, and a distribution-uniform strong Gaussian approximation.