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dc.contributor.authorKinnear, Ryan
dc.date.accessioned2017-08-31 19:35:50 (GMT)
dc.date.available2017-08-31 19:35:50 (GMT)
dc.date.issued2017-08-31
dc.date.submitted2017-08-30
dc.identifier.urihttp://hdl.handle.net/10012/12316
dc.description.abstractIn this thesis we study the notion of Granger-causality, a statistical concept originally developed to estimate causal effects in econometrics. First, we suggest a more general notion of Granger-causality in which to frame the proceeding practical developments. And second, we derive a proximal optimization algorithm to fit large and sparse vector autoregressive models, a task closely connected to the estimation Granger-causality amongst jointly wide sense stationary process. Experimental results from our so called “Depth Wise Grouped LASSO” convex program are obtained for both simulated data, as well as Canadian meteorology data. We conclude by discussing some applications and by suggesting future research questions.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectTime Seriesen
dc.subjectGranger Causalityen
dc.subjectSparsityen
dc.subjectGrouped LASSOen
dc.subjectConvex Optimizationen
dc.titleGranger Causal Network Learning and the Depth Wise Grouped LASSOen
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentElectrical and Computer Engineeringen
uws-etd.degree.disciplineElectrical and Computer Engineeringen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Applied Scienceen
uws.contributor.advisorMazumdar, Ravi
uws.contributor.affiliation1Faculty of Engineeringen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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