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Non-parametric Wiener-Granger Causality in Partially Observed Systems
Stockholm University, Faculty of Science, Department of Mathematics.ORCID iD: 0000-0001-7194-7996
2014 (English)In: IEEE 2014 Conference on Norbert Wiener in the 21st Century: Driving Technology's Future, 2014Conference paper, Oral presentation only (Refereed)
Abstract [en]

Wiener’s definition of causality, now known as Wiener-Granger causality, has become a frequently used quantification of temporally resolved causality in numerous fields of science. In many empirical studies, the system of interest cannot be observed in its entirety and the observations may include non-informative data. To this end, partial Wiener-Granger causality has been developed to circumvent this issue. In this paper, we extend partial Wiener-Granger causality to the non-parametric case and discuss two approaches to obtain estimates of this non-parametric, entropy-based measure.

Place, publisher, year, edition, pages
2014.
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:su:diva-101592OAI: oai:DiVA.org:su-101592DiVA: diva2:704418
Conference
IEEE 2014 Conference on Norbert Wiener in the 21st Century 24-26 June 2014, Boston MA
Note

The paper is accepted to the conference  mentioned dabove

Available from: 2014-03-12 Created: 2014-03-12 Last updated: 2014-03-25Bibliographically approved
In thesis
1. A Treatise on Measuring Wiener-Granger Causality
Open this publication in new window or tab >>A Treatise on Measuring Wiener-Granger Causality
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Wiener-Granger causality is a well-established concept of causality based on stochasticity and the flow of time, with applications in a broad array of quantitative sciences. The majority of methods used to measure Wiener-Granger causality are based on linear premises and hence insensitive to non-linear signals. Other frameworks based on non-parametric techniques are often computationally expensive and susceptible to overfitting or lack of sensitivity.

In this thesis, Paper I investigates the application of linear Wiener-Granger causality to migrating cancer cell data obtained using a Systems Microscopy experimental platform. Paper II represents a review of non-parametric measures based on information theory and discusses a number of related bottlenecks and potential routes of circumvention. Paper III studies the properties of a frequently used non-parametric information theoretical measure for a class of non-Gaussian distributions. Paper IV introduces a new efficient scheme for non-parametric analysis of Wiener-Granger causality based on kernel canonical correlations, and studies the connection between this new scheme and the information theoretical approach. Lastly, Paper V draws upon the results in the preceding paper to discuss non-parametric analysis of Wiener-Granger causality in partially observed systems.

Altogether, the work presented in this thesis constitutes a comprehensive review on measures of Wiener-Granger causality in general, and in particular, features new insights on efficient non-parametric analysis of Wiener-Granger causality in high-dimensional settings.

Place, publisher, year, edition, pages
Stockholm: Department of Mathematics, Stockholm University, 2014. 44 p.
Keyword
Wiener-Granger causality, Information theory, Kernel canonical correlation, Systems Microscopy, Cell migration.
National Category
Probability Theory and Statistics Cell Biology
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:su:diva-101595 (URN)978-91-7447-861-7 (ISBN)
Public defence
2014-04-16, sal 14 hus 5, Kräftriket, Roslagsvägen 101, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework ProgrammeSwedish Research Council
Note

At the time of the doctoral defence, the following papers were unpublished and had a status as follows: Paper 3: Accepted Paper 4: Manuscript; Paper 5: Accepted

Available from: 2014-03-25 Created: 2014-03-12 Last updated: 2014-03-13Bibliographically approved

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CiteExportLink to record
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