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  Event coincidence analysis for quantifying statistical interrelationships between event time series

Donges, J. F., Schleussner, C.-F., Siegmund, J. F., Donner, R. V. (2016): Event coincidence analysis for quantifying statistical interrelationships between event time series. - European Physical Journal - Special Topics, 225, 3, 471-487.
https://doi.org/10.1140/epjst/e2015-50233-y

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 Creators:
Donges, Jonathan Friedemann1, Author              
Schleussner, Carl-Friedrich1, Author              
Siegmund, Jonatan F.1, Author              
Donner, Reik V.1, Author              
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1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Studying event time series is a powerful approach for analyzing the dynamics of complex dynamical systems in many fields of science. In this paper, we describe the method of event coincidence analysis to provide a framework for quantifying the strength, directionality and time lag of statistical interrelationships between event series. Event coincidence analysis allows to formulate and test null hypotheses on the origin of the observed interrelationships including tests based on Poisson processes or, more generally, stochastic point processes with a prescribed inter-event time distribution and other higher-order properties. Applying the framework to country-level observational data yields evidence that flood events have acted as triggers of epidemic outbreaks globally since the 1950s. Facing projected future changes in the statistics of climatic extreme events, statistical techniques such as event coincidence analysis will be relevant for investigating the impacts of anthropogenic climate change on human societies and ecosystems worldwide.

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 Dates: 2016
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1140/epjst/e2015-50233-y
PIKDOMAIN: Earth System Analysis - Research Domain I
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 7178
Research topic keyword: Climate impacts
Research topic keyword: Extremes
Research topic keyword: Health
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
Model / method: Open Source Software
Regional keyword: Global
Organisational keyword: FutureLab - Earth Resilience in the Anthropocene
Organisational keyword: RD1 - Earth System Analysis
Organisational keyword: RD4 - Complexity Science
Working Group: Whole Earth System Analysis
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: European Physical Journal - Special Topics
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 225 (3) Sequence Number: - Start / End Page: 471 - 487 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/150617