##Statistics Seminar##\\ Department of Mathematical Sciences
^ **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 |
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**Abstract**
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.