Model selection with Lasso-Zero and a robust extension with an application to the problem of missing covariates

With Sylvain Sardy, Université de Genève

Model selection with Lasso-Zero and a robust extension with an application to the problem of missing covariates

We propose a new model selection technique based on the limit of the lasso path as the penalty parameter tends to zero. The method provably guarantees model selection under a weaker condition than the lasso and performs better empirically in terms of false discovery rate (FDR). We extend the method to the situation of missing covariates.

Add to your calendar or Include in your list

How can mathematics help us to understand the behaviour of ants? Read more about the fanscinating work being carri… https://t.co/iCODvvxqE6 View on Twitter