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dc.contributor.authorHu, Yang
dc.date.accessioned2016-04-04T18:06:34Z
dc.date.available2021-02-01T07:00:38Z
dc.date.issued2016-02-01
dc.identifier.otherbibid: 9597293
dc.identifier.urihttps://hdl.handle.net/1813/43737
dc.description.abstractI will introduce a group lasso algorithm for complex variables and demonstrate its application to a novel time series model called tensor autoregression (T-AR). T-AR utilizes the t-product tensor operation on 3-dimensional tensors and models time series exhibiting seasonality and geometric trend. The tensor structure of T-AR enables historical information from a selection of lag durations to simultaneously affect the prediction of a single series. I will first introduce the topic of tensor computations and motivate the t-product and T-SVD manipulations. Then, I will derive the T-AR model from the t-product definition and discuss its properties and interpretation. Next, I will adapt a group lasso algorithm to complex-valued problems and derive the fast algorithm for lag selection in T-AR. Finally, I will conclude with simulation results.
dc.language.isoen_US
dc.titleComplex-Valued Group Lasso For Tensor Autoregressive Models
dc.typedissertation or thesis
thesis.degree.disciplineStatistics
thesis.degree.grantorCornell University
thesis.degree.levelMaster of Science
thesis.degree.nameM.S., Statistics
dc.contributor.chairWells,Martin Timothy
dc.contributor.committeeMemberVan Loan,Charles Francis


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