ArticleScience advances2024
Causes of extreme events revealed by Rényi information transfer.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Spatial Flows of Information Entropy as Indicators of Climate Variability and Extremes.Entropy (Basel, Switzerland) · 2025Article
- Signatures of Extreme Events in Cumulative Entropic Spectrum.Entropy (Basel, Switzerland) · 2025Article
- Electrophysiological Alterations in the Progression of Parkinson's Disease and the Therapeutic Effect of Tetrabenazine on Rats With Levodopa-Induced Dyskinesia.CNS neuroscience & therapeutics · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Information-theoretic generalization of Granger causality principle, based on evaluation of conditional mutual information, also known as transfer entropy (CMI/TE), is redefined in the framework of Rényi entropy (RCMI/RTE). Using numerically generated data with a defined causal structure and examples of real data from the climate system, it is demonstrated that RCMI/RTE is able to identify the cause variable responsible for the occurrence of extreme values in an effect variable. In the presented example, the Siberian High was identified as the cause responsible for the increased probability of cold extremes in the winter and spring surface air temperature in Europe, while the North Atlantic Oscillation and blocking events can induce shifts of the whole temperature probability distribution.
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Registered trials
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