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UID:20260602T085052EDT-6654AEI5mn@132.216.98.100
DTSTAMP:20260602T125052Z
DESCRIPTION:Penalized Robust Regression Estimation with Applications to Pro
 teomics\n\nIn many current applications scientists can easily measure a ve
 ry large number of variables (for example\, hundreds of protein levels) so
 me of which are expected be useful to explain or predict a specific respon
 se variable of interest. These potential explanatory variables are most li
 kely to contain redundant or irrelevant information\, and in many cases\, 
 their quality and reliability may be suspect. \n\nWe developed two penaliz
 ed robust regression estimators that can be used to identify a useful subs
 et of explanatory variables to predict the response\, while protecting the
  resulting estimator against possible aberrant observations in the data se
 t. Using an elastic net penalty\, the proposed estimator can be used to se
 lect variables\, even in cases with more variables than observations or wh
 en many of the candidate explanatory variables are correlated. In this tal
 k\, I will present the new estimator and an algorithm to compute it. I wil
 l also illustrate its performance in a simulation study and a real data se
 t. This is joint work with Professor Matias Salibian-Barrera\, my PhD stud
 ent David Kepplinger\, and my PDF Ezequiel Smuggler.\n\n \n
DTSTART:20171027T193000Z
DTEND:20171027T203000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Gabriela Cohen Freue (University of British Columbia)
URL:/mathstat/channels/event/gabriela-cohen-freue-univ
 ersity-british-columbia-281981
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