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--- seminars:stat:201008 [2020/10/06 16:38] – qyu
+++ seminars:stat:201008 [2020/10/07 15:27] (current) – qiao
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+<WRAP 70% center>
+^  **DATE:**|Thursday, October 8, 2020 |
+^  **TIME:**|1:15pm -- 2:15pm |
+^  **LOCATION:**|Zoom meeting |
+^  **SPEAKER:**|Xinhai Zhang, Binghamton University |
+^  **TITLE:**|Outcome Weighted Learning for Optimal Treatment Regimes  |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+ There is increasing interest in discovering individualized treatment
+rules for patients who have heterogeneous responses to treatment. In particular, one
+aims to find an optimal individualized treatment rule which is a deterministic
+function of patient specific characteristics maximizing expected clinical
+outcome. Zhao et al. (2012) shown that estimating such an optimal treatment regime
+is equivalent to a classification problem where each subject is weighted
+proportional to his or her clinical outcome. Then they propose an outcome weighted
+learning (OWL) approach based on the support vector machine framework. A few other
+development after the original OWL will also be in this talk.
+</WRAP>