[Statlist] Next talk: Friday, 07.12.2018, with Pascaline Descloux Université de Genève

[Maurer Letizia]

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ETH and University of Zurich

Organisers:

Proff. P. Bühlmann - L. Held - T. Hothorn - M. Maathuis -
N. Meinshausen - S. van de Geer - M. Wolf

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We are glad to announce the following talk:

Friday, 07.12.2018, at 15.00 h  ETH Zurich HG G19.1
with Pascaline Descloux Université de Genève

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Title:

Model selection with Lasso-Zero <https://www.math.ethz.ch/sfs/news-and-events/research-seminar.html?s=hs18#e_12119>

Abstract:

In the problem of variable selection in high-dimensional linear regression, the Lasso is known to require a strong condition on the design matrix and the true support of the regression coefficients in order to recover the true set of important variables. This difficulty being attributed to an excessive amount of shrinkage, multistage procedures and nonconcave penalties have been introduced to address this issue. We rather propose another approach called Lasso-Zero, based on the limit solution of Lasso as its tuning parameter tends to zero, in other words where Lasso's shrinkage effect is the weakest. Since this provides an overfitted model, Lasso-Zero relies on the generation of several random noise dictionaries concatenated to the design matrix. The obtained coefficients are thresholded by a parameter tuned by Quantile Universal Thresholding (QUT). We prove that under some beta-min condition, a simplified version of Lasso-Zero recovers the true model under a weaker condition on the design matrix than Lasso, and that it controls the FDR in the orthonormal case if it is tuned by QUT. Numerical experiments show that Lasso-Zero outperforms its competitors in terms of FDR/TPR tradeoff and exact model recovery.

This abstract is also to be found under the following link: http://stat.ethz.ch/events/research_seminar

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