Complex-Valued Group Lasso For Tensor Autoregressive Models
| dc.contributor.author | Hu, Yang | |
| dc.contributor.chair | Wells,Martin Timothy | |
| dc.contributor.committeeMember | Van Loan,Charles Francis | |
| dc.date.accessioned | 2016-04-04T18:06:34Z | |
| dc.date.available | 2021-02-01T07:00:38Z | |
| dc.date.issued | 2016-02-01 | |
| dc.description.abstract | I 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.identifier.other | bibid: 9597293 | |
| dc.identifier.uri | https://hdl.handle.net/1813/43737 | |
| dc.language.iso | en_US | |
| dc.title | Complex-Valued Group Lasso For Tensor Autoregressive Models | |
| dc.type | dissertation or thesis | |
| thesis.degree.discipline | Statistics | |
| thesis.degree.grantor | Cornell University | |
| thesis.degree.level | Master of Science | |
| thesis.degree.name | M.S., Statistics |
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