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Statistics Seminar

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.

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