Abstract Details
Activity Number:
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20
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Mental Health Statistics Section
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Abstract - #307929 |
Title:
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Alternative Methods for Bayesian Variable Selection in Binomial Regression Models with Missing Covariates
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Author(s):
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Xiaowei Yang*+ and Gang Liu and Thomas R. Belin
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Companies:
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CUNY-Hunter College and Google and UCLA Department of Biostatistics
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Keywords:
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Bayesian Variable Selection ;
Model Uncertainty ;
Binomial Regression ;
Logistic regression
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Abstract:
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A crucial problem in building a multiple regression model is the selection of predictors to include. Logistic regression model is a popular modeling approach to find the relationship between a binary response variable and a subset of potential explanatory variables or predictors. In practice, it is common for there to be numerous variables measured and many of them have missing values. When some cases are incomplete traditional data analysis methods can provide biased estimators and invalid inferences if incomplete cases are excluded from consideration. In this paper we develop two strategies for variable selection in logistic regression models with missing values on explanatory variables. One approach, which we call "impute, then select" (ITS), involves initially performing multiple imputation and then applying Bayesian variable selection to the multiply imputed data sets. The second strategy, which we call "simultaneously impute and select" (SIAS), is to conduct Bayesian variable selection and missing data imputation simultaneously within one Markov Chain Monte Carlo (MCMC) process. For illustration, both simulated and practical data sets will be analyzed.
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