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Durham University

Department of Mathematical Sciences


This week's seminars

Stats4Grads: A Sensitivity Analysis of Adaptive Lasso

Presented by Tathagata Basu, Durham University

13 November 2019 13:00 in CM105

Sparse regression is an effcient statistical modelling technique which is of major relevance for high dimensional statistics. There are several ways of achieving sparse regression, the well-known lasso being one of them. However, lasso variable selection may not be consistent in selecting the true sparse model. Zou proposed an adaptive form of the lasso which overcomes this issue, and showed that data driven weights on the penalty term will result in a consistent variable selection procedure. We are interested in the case that the weights are informed by a prior execution of ridge regression. We carry out a sensitivity analysis of the Adaptive lasso through the power parameter of the weights, and demonstrate that, in effect, this parameter takes over the role of the usual lasso penalty parameter. In addition, we use the parameter as an input variable to obtain an error bound on the Adaptive lasso.

Contact for more information

See the Stats4Grads page for more details about this series.

Research Seminars by Series

The research groups in the Department of Mathematical Sciences hold several seminar series in term time. Information on date, time and location are available here.

For information on previous years' seminars please see the seminar archives pages.