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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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  • UserSylvain Sardy, Université de Genève
  • ClockFriday 01 February 2019, 16:00-17:00
  • HouseMR12.

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.

This talk is part of the Statistics series.

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