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Talk
Title	High-dimensional model estimation and model selection
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Author(s)	MUELLER, Christian (speaker) (Simons Foundation)
Corporate author(s)	CERN. Geneva
Imprint	2015-11-12. - 2344.
Series	(LPCC Workshops) (Data Science @ LHC 2015 Workshop)
Lecture note	on 2015-11-12T14:00:00
Subject category	LPCC Workshops
Abstract	I will review concepts and algorithms from high-dimensional statistics for linear model estimation and model selection. I will particularly focus on the so-called p>>n setting where the number of variables p is much larger than the number of samples n. I will focus mostly on regularized statistical estimators that produce sparse models. Important examples include the LASSO and its matrix extension, the Graphical LASSO, and more recent non-convex methods such as the TREX. I will show the applicability of these estimators in a diverse range of scientific applications, such as sparse interaction graph recovery and high-dimensional classification and regression problems in genomics.
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Submitted by	[email protected]