This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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112
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 2, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #306847 |
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 E. Raftery and David Madigan and Randall Burd
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Companies:
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Columbia University and University of Washington and Columbia University and Children's National Medical Center
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Address:
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Room 1005 SSW, MC 4690, New York, NY, 10027,
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Keywords:
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Dynamic modeling ;
Online classification
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Abstract:
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We propose an online binary classication procedure for cases when parameters within a model change over time. We account for model uncertainty through Bayesian Model Averaging and allow model probabilities to also change with time. We apply a state-space model to the parameters of each model and a Markov chain model to the correct model, allowing our "correct" model to change over time. Our model accommodates different levels of change in the data-generating mechanism through a "forgetting" factor. A novel tuning algorithm which adjusts the level of forgetting in a completely online fashion using the posterior predictive distribution allows the model to accommodate various levels of change in the date-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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