Kullback-Leibler Information for Model Selection and Comparison in Logistic Linear Models

 

Host Institution:

La Trobe University

Title of Seminar:

Kullback-Leibler 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 mis-specification?

(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 Kullback-Leibler 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

 

Seminar Convenor:

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