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Activity Number: 37
Type: Contributed
Date/Time: Sunday, August 2, 2009 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304024
Title: Dynamic Logistic Regression and Dynamic Model Averaging for Binary Classification
Author(s): Tyler H. McCormick*+ and Adrian E. Raftery and David Madigan and Randall Burd
Companies: Columbia University and University of Washington and Columbia University and Children's National Medical Center
Address: Room 1005 SSW, MC 4690, New York, NY, 10025,
Keywords: Dynamic Model Averaging ; Binary Classification
Abstract:

We present a model for dynamic model averaging for binary classification. The model accounts for model uncertainty when parameters are expected to change over time by applying a Markov chain model to the "correct" model and a state space model for the parameters within each model. Parameterization is done in terms of forgetting and approximation of the marginal likelihood is computed via Laplace Approximation. We evaluate the method using simulated data and present an application to laparoscopic surgery in children.

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