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DTSTAMP:20250916T094626Z
DESCRIPTION:Post Selection Statistical Learning in High Dimensional Data\n
 \n \n\nNowadays a large amount of data is available\, and the need for nov
 el statistical strategies to analyze such data sets is pressing. This talk
  focusses on the development of statistical and computational strategies f
 or a sparse regression model in the presence of mixed signals. The existin
 g estimation methods have often ignored contributions from weak signals. H
 owever\, in reality\, many predictors altogether provide useful informatio
 n for prediction\, although the amount of such useful information in a sin
 gle predictor might be modest. The search for such signals\, sometimes cal
 led networks or pathways\, is for instance an important topic for those wo
 rking on personalized medicine. We discuss a new “post selection shrinkage
  estimation strategy” that takes into account the joint impact of both str
 ong and weak signals to improve the prediction accuracy\, and opens pathwa
 ys for further research in such scenarios\n
DTSTART:20170912T193000Z
DTEND:20170912T203000Z
SUMMARY:Ejaz Ahmed\, Brock University
URL:/mathstat/channels/event/ejaz-ahmed-brock-universi
 ty-270172
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