Conditional independence testing with a single realization of a multivariate nonstationary nonlinear time series
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal DiscoveryTime SeriesTime Series AnalysisVariable SelectionSimilar Papers 제목 키워드 기반
Predictive Independence Testing, Predictive Conditional Independence Testing, and Predictive Graphical Modelling
Testing (conditional) independence of multivariate random variables is a task central to statistical inference and modelling in general - though unfortunately one for which to date there does not exist a practicable work…
PhilosophyConditional Independence Test Based on Transport Maps
Testing conditional independence between two random vectors given a third is a fundamental and challenging problem in statistics, particularly in multivariate nonparametric settings due to the complexity of conditional s…
Testing Conditional Mean Independence Using Generative Neural Networks
Conditional mean independence (CMI) testing is crucial for statistical tasks including model determination and variable importance evaluation. In this work, we introduce a novel population CMI measure and a bootstrap-bas…
Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence
As a crucial problem in statistics is to decide whether additional variables are needed in a regression model. We propose a new multivariate test to investigate the conditional mean independence of Y given X conditioning…
copent: Estimating Copula Entropy and Transfer Entropy in R
Statistical independence and conditional independence are two fundamental concepts in statistics and machine learning. Copula Entropy is a mathematical concept defined by Ma and Sun for multivariate statistical independe…
BIG-bench Machine LearningCausal DiscoveryMutual Information Estimationstatistical independence testing+1