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

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Sylvain Sardy, Université de Genève
Friday 01 February 2019, 16:00-17:00
MR12.

If you have a question about this talk, please contact Dr Sergio Bacallado .

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

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

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

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

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