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DESCRIPTION:\n	New Approaches for Inference on Optimal Treatment Regimes\n\n
 	 \n\n	Abstract:\n\n\nFinding the optimal treatment regime (or a series of s
 equential treatment regimes) based on individual characteristics has impor
 tant applications in precision medicine. We propose two new approaches to 
 quantify uncertainty in optimal treatment regime estimation. First\, we co
 nsider inference in the model-free setting\, which does not require specif
 ying an outcome regression model. Existing model-free estimators for optim
 al treatment regimes are usually not suitable for the purpose of inference
 \, because they either have nonstandard asymptotic distributions or do not
  necessarily guarantee consistent estimation of the parameter indexing the
  Bayes rule due to the use of surrogate loss. We study a smoothed robust e
 stimator that directly targets the parameter corresponding to the Bayes de
 cision rule for optimal treatment regimes estimation. We verify that a res
 ampling procedure provides asymptotically accurate inference for both the 
 parameter indexing the optimal treatment regime and the optimal value func
 tion. Next\, we consider the high-dimensional setting and propose a semipa
 rametric model-assisted approach for simultaneous inference. Simulation re
 sults and real data examples are used for illustration.\n\n\n	Speaker\n\n\n
 Dr. Lan Wang is a Professor from the Department of Management Science at t
 he Miami Herbert Business School of the University of Miami\, with a secon
 dary appointment as Professor of Public Health Sciences at the Miller Scho
 ol of Medicine\, University of Miami. She currently serves as the Co-Edito
 r for Annals of Statistics (2022-2024)\, jointly with Professor Enno Mamme
 n.\n\nDr. Wang’s research covers several interrelated areas: high-dimensio
 nal statistical learning\, quantile regression\, optimal personalized deci
 sion recommendation\, and survival analysis. She is also interested in int
 erdisciplinary collaboration\, driven by applications in healthcare\, busi
 ness\, economics\, and other domains.\n\nBefore joining University of Miam
 i\, she was a Professor of Statistics at School of Statistics\, University
  of Minnesota. She got her Ph.D. in Statistics from the Pennsylvania State
  University. She got her Bachelor’s degree in Applied Mathematics from Tsi
 nghua University\, China.\n\nDr. Wang is an elected Fellow of the American
  Statistical Association\, an elected Fellow of the Institute of Mathemati
 cal Statistics\, and an elected member of the International Statistical In
 stitute. She was the associate editor for several leading statistical jour
 nals: Journal of the American Statistical Associations\, Annals of Statist
 ics\, Journal of the Royal Statistics Society\, and Biometrics.\n\nMcGill 
 Statistics Seminar schedule: https://mcgillstat.github.io/\n\nhttps://mcgi
 ll.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09\n\n\n	 \n\n\n
 	\n		\n			 \n		\n	\n\n
DTSTART:20220311T203000Z
DTEND:20220311T213000Z
SUMMARY:Lan Wang (University of Miami)
URL:https://www.mcgill.ca/mathstat/channels/event/lan-wang-university-miami
 -338354
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