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SUMMARY:High-dimensional model estimation and model selection
DTSTART;VALUE=DATE-TIME:20151112T130000Z
DTEND;VALUE=DATE-TIME:20151112T134500Z
DTSTAMP;VALUE=DATE-TIME:20201123T230205Z
UID:indico-contribution-939879@indico.cern.ch
DESCRIPTION:Speakers: Christian Mueller (Simons Foundation)\nI will review
concepts and algorithms from high-dimensional statistics for linear model
estimation and model selection. I will particularly focus on the so-calle
d p>>n setting where the number of variables p is much larger than the num
ber of samples n. I will focus mostly on regularized statistical estimator
s that produce sparse models. Important examples include the LASSO and its
matrix extension\, the Graphical LASSO\, and more recent non-convex metho
ds such as the TREX. I will show the applicability of these estimators in
a diverse range of scientific applications\, such as sparse interaction gr
aph recovery and high-dimensional classification and regression problems i
n genomics.\n\nhttps://indico.cern.ch/event/395374/contributions/939879/
LOCATION:CERN 222/R-001
URL:https://indico.cern.ch/event/395374/contributions/939879/
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