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Abstract Details
Activity Number:
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223
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 1, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #301030 |
Title:
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Dynamic Logistic Regression and Dynamic Model Averaging for Binary Classification
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Author(s):
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Tyler McCormick*+ and Adrian Raftery and David Madigan and Randall Burd
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Companies:
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University of Washington and University of Washington and Columbia University and Children's National Medical Center
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Address:
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Box 354320, Seattle, WA, 98195-4320, United States
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Keywords:
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Bayesian model averaging ;
Binary classification ;
Confidentiality ;
Hidden Markov model ;
Laparoscopic surgery ;
Markov chain
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Abstract:
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We propose an online binary classification procedure for cases when there is uncertainty about the model to use and when parameters within a model change over time. We account for model uncertainty through Dynamic Model Averaging (DMA), a dynamic extension of Bayesian Model Averaging (BMA) in which posterior model probabilities are also allowed to change with time. We do this by applying a state-space model to the parameters of each model and a Markov chain model to the data-generating model, allowing the "correct'' model to change over time. Our method accommodates different levels of change in the data-generating mechanism by calibrating a "forgetting'' factor. We propose an algorithm which adjusts the level of forgetting in a completely online fashion using the posterior predictive distribution. Our algorithm allows the model to accommodate various levels of change in the data-generating mechanism at different times. We apply our method to data from children with appendicitis who receive either a traditional (open) appendectomy or a laparoscopic procedure.
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