Causal information approach to partial conditioning in multivariate data sets

DOI10.1155/2012/303601zbMath1244.62126arXiv1111.0680OpenAlexW2592846171WikidataQ34295393 ScholiaQ34295393MaRDI QIDQ428232

D. Marinazzo, Sebastiano Stramaglia, Mario Pellicoro

Publication date: 19 June 2012

Published in: Computational \& Mathematical Methods in Medicine (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1111.0680

Mathematics Subject Classification ID

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Lasso Granger Causal Models: Some Strategies and Their Efficiency for Gene Expression Regulatory Networks ⋮ Assessing the strength of cardiac and sympathetic baroreflex controls via transfer entropy during orthostatic challenge ⋮ Causation entropy from symbolic representations of dynamical systems ⋮ Two stage approach to functional network reconstruction for binary time-series ⋮ Shortcomings of Transfer Entropy and Partial Transfer Entropy: Extending Them to Escape the Curse of Dimensionality ⋮ Detecting direct causality in multivariate time series: a comparative study

Cites Work

• Grouping time series by pairwise measures of redundancy
• Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance
• Predictable processes and Wold's decomposition: A review
• Measures of Conditional Linear Dependence and Feedback Between Time Series
• Investigating Causal Relations by Econometric Models and Cross-spectral Methods

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