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DTSTAMP:20260605T134238Z
DESCRIPTION:Selective inference for dynamic treatment regimes via the LASSO
 \n\n\n	Abstract:\n\n\nConstructing an optimal dynamic treatment regime beco
 me complex when there are large number of prognostic factors\, such as pat
 ient’s genetic information\, demographic characteristics\, medical history
  over time. Existing methods only focus on selecting the important variabl
 es for the decision-making process and fall short in providing inference f
 or the selected model. We fill this gap by leveraging the conditional sele
 ctive inference methodology. We show that the proposed method is asymptoti
 cally valid given certain rate assumptions in semiparametric regression.\n
 \n\n	Speaker\n\n\nAshkan Ertefaie is an Assistant Professor in the Dept of 
 Biostatistics and Computational Biology at the the University of Rochester
 . He is a 91ºÚÁÏÍø alumnus with a PhD degree in Statistics\, under co-superv
 ision of Professors David Stephens and Masoud Asgharian. His research inte
 rest lies in causal inference\, dynamic treatment regimes\, sequential mul
 tiple assignment randomized trials\, comparative effectiveness studies usi
 ng electronic health records\, instrumental variable analyses\, high-dimen
 sional data analysis\, post selection inference\, and survival analysis.\n
DTSTART:20180928T193000Z
DTEND:20180928T203000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Ashkan Ertefaie
URL:/mathstat/channels/event/ashkan-ertefaie-290122
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