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--- seminars:stat:201015 [2020/10/10 17:32] – qyu
+++ seminars:stat:201015 [2020/10/10 17:34] (current) – qyu
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+<WRAP 70% center>
+^  **DATE:**|Thursday, Oct. 15, 2020 |
+^  **TIME:**|1:15pm -- 2:15pm |
+^  **LOCATION:**|Zoom meeting |
+^  **SPEAKER:**|Wenshu Dai, Binghamton University |
+^  **TITLE:**|Concomitant variables in finite mixture models  |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+The standard mixture model, the concomitant variable mixture
+model, the mixture regression model and the concomitant variable mixture
+regression model all enable simultaneous identification and description
+of groups of observations. This study reviews the different ways in
+which dependencies among the variables involved in these models are
+accommodated. It is demonstrated that the standard and the concomitant
+variable mixture models identify groups of observations and at the same
+time discriminate them analogous, respectively, to discriminant analysis
+and logistic regression. While the mixture regression model is shown to
+have limited use for classifying new observations. An extension of it,
+called the saturated mixture regression model, is shown to be useful in
+that respect. Advantages of that model in model estimation when missing
+data are present and as a framework for model selection are also
+discussed.
+</WRAP>