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Exploratory Causal Analysis in Bivariate Time Series Data

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dc.contributor.advisor Weigel, Robert S. McCracken, James M.
dc.creator McCracken, James M. 2016-04-19T19:28:48Z 2016-04-19T19:28:48Z 2015
dc.description.abstract Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments and data analysis techniques are required for identifying causal information and relationships directly from observational data. This need has lead to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics.
dc.format.extent 172 pages
dc.language.iso en
dc.rights Copyright 2015 James M. McCracken
dc.subject Physics en_US
dc.subject causality en_US
dc.subject Granger en_US
dc.subject leaning en_US
dc.subject pairwise asymmetric inference en_US
dc.subject penchant en_US
dc.subject time series analysis en_US
dc.title Exploratory Causal Analysis in Bivariate Time Series Data
dc.type Dissertation en Doctoral en Physics en George Mason University en

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