A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression
| Autor: | Nicoletta Nicolaou, Timothy G. Constandinou |
|---|---|
| Přispěvatelé: | Commission of the European Communities |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Multivariate statistics
conditional causality Computer science Biomedical Engineering Neuroscience (miscellaneous) Machine learning computer.software_genre 01 natural sciences Causality (physics) 03 medical and health sciences 0302 clinical medicine Granger causality 0103 physical sciences Econometrics Methods 010306 general physics nonparametric causality nonparametric multiplicative regression business.industry Nonparametric statistics Estimator nonlinear causality Regression Computer Science Applications Autoregressive model multivariate causality Pairwise comparison Artificial intelligence business computer 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | Frontiers in Neuroinformatics |
| ISSN: | 1662-5196 |
| Popis: | Causal prediction has become a popular tool for neuroscience applications, as it allows the study of relationships between different brain areas during rest, cognitive tasks or brain disorders. We propose a nonparametric approach for the estimation of nonlinear causal prediction for multivariate time series. In the proposed estimator, C NPMR , Autoregressive modeling is replaced by Nonparametric Multiplicative Regression (NPMR). NPMR quantifies interactions between a response variable (effect) and a set of predictor variables (cause); here, we modified NPMR for model prediction. We also demonstrate how a particular measure, the sensitivity Q, could be used to reveal the structure of the underlying causal relationships. We apply C NPMR on artificial data with known ground truth (5 datasets), as well as physiological data (2 datasets). C NPMR correctly identifies both linear and nonlinear causal connections that are present in the artificial data, as well as physiologically relevant connectivity in the real data, and does not seem to be affected by filtering. The Sensitivity measure also provides useful information about the latent connectivity.The proposed estimator addresses many of the limitations of linear Granger causality and other nonlinear causality estimators. C NPMR is compared with pairwise and conditional Granger causality (linear) and Kernel-Granger causality (nonlinear). The proposed estimator can be applied to pairwise or multivariate estimations without any modifications to the main method. Its nonpametric nature, its ability to capture nonlinear relationships and its robustness to filtering make it appealing for a number of applications. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|