This week's seminars
Stats4Grads: A Sensitivity Analysis of Adaptive Lasso
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 firstname.lastname@example.org for more information
See the Stats4Grads page for more details about this series.