Host Institution: 
La Trobe University 
Title of Seminar: 
KullbackLeibler Information for Model Selection and Comparison in Logistic Linear Models 
Speaker's Name: 
Guoqi Qian 
Speaker's Institution: 
University of Melbourne 
Time and Date: 
Friday 5 June 2009 at 11:30 am 
Seminar Abstract: 
We consider two issues involved in model selection. (1) Model selection starts with a proposed model class, and it is often unrealistically assumed that the true model generating the data belongs to this model class. Then what would happen to model selection if the true model is not in the proposed model class? In other words, how to quantify the model selection bias in the situation of model class misspecification? (2) Model selection often ends up with a selected optimum model minimizing or maximizing a numeric selection criterion function. But it does not or is not able to provide a measure of variability or uncertainty involved in model selection. Such a measure, if available, would be very useful in determining models which are indistinguishable from the optimum model. We have developed new estimators of KullbackLeibler information to address these two issues. Our work will be presented in the context of logistic regression model selection but can be extended to other model

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