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dc.contributor.advisorTarokh, Vahid
dc.contributor.authorDing, Jie
dc.date.accessioned2018-12-20T08:11:27Z
dc.date.created2017-03
dc.date.issued2017-01-27
dc.date.submitted2017
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:37945007*
dc.description.abstractIn spite of substantial results in time series analysis, there remain many unsolved problems and challenges in design of generally applicable prediction systems. In this dissertation, we address some of the challenges. We present some new techniques and analysis toward optimal model selection in well/mis-specified model classes, modeling of nonlinearity, high dimensionality, structure changes, recurring patterns, etc.
dc.description.sponsorshipEngineering and Applied Sciences - Engineering Sciences
dc.format.mimetypeapplication/pdf
dc.language.isoen
dash.licenseLAA
dc.subjectStatistics
dc.subjectEngineering, Electronics and Electrical
dc.subjectMathematics
dc.titleNonlinear Modeling and Prediction for Time Series
dc.typeThesis or Dissertation
dash.depositing.authorDing, Jie
dc.date.available2018-12-20T08:11:27Z
thesis.degree.date2017
thesis.degree.grantorGraduate School of Arts & Sciences
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
dc.contributor.committeeMemberJacob, Pierre E.
dc.contributor.committeeMemberYang, Yuhong
dc.contributor.committeeMemberKou, Samuel
dc.contributor.committeeMemberBrockett, Roger W.
dc.type.materialtext
thesis.degree.departmentEngineering and Applied Sciences - Engineering Sciences
dash.identifier.vireohttp://etds.lib.harvard.edu/gsas/admin/view/1364
dc.description.keywordstime series; prediction; inference; model selection; information criterion; nonlinear system; high dimensionality; change detection; recurring patterns
dc.identifier.orcid0000-0002-3584-6140
dash.author.emaildjrlthu@gmail.com


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