This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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353
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract - #308605 |
Title:
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Using Hybrid MCMC for Logistic Regression Model Selection
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Author(s):
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Robert Feyerharm*+
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Companies:
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Independent Consultant
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Address:
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1000 NE 10th St, Oklahoma City, OK, 73117,
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Keywords:
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Markov chain Monte Carlo ;
logistic regression ;
Akaike Information Criterion
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Abstract:
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Logistic regression models are used in many fields to study binary or proportional outcome variables. Selecting the optimal logistic regression model from among a large number of candidate variables poses a challenging computational problem. In this paper I propose using the hybrid MCMC method to search a finite parameter space of candidate explanatory variables with the goal of maximizing a target density constructed from a normalized AIC function. Variables are eliminated from the final model where the MCMC procedure converges to a ßk=0 solution. To ensure differentiability and proper convergence properties, the AIC function is smoothed in the vicinity of degenerate points (ßk=0). Simulation data is generated to assess the convergence and efficiency of the MCMC procedure.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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