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Exploratory Causal Analysis in Bivariate Time Series Data

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dc.contributor.advisor Weigel, Robert S.
dc.contributor.author McCracken, James M.
dc.creator McCracken, James M.
dc.date.accessioned 2016-04-19T19:28:48Z
dc.date.available 2016-04-19T19:28:48Z
dc.date.issued 2015
dc.identifier.uri https://hdl.handle.net/1920/10169
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
thesis.degree.level Doctoral en
thesis.degree.discipline Physics en
thesis.degree.grantor George Mason University en


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