Bayesian Binomial Regression: Predicting Survival at a Trauma Center |
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Authors: | Edward J. Bedrick Ronald Christensen Wesley Johnson |
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Affiliation: | 1. Department of Statistics , University of New Mexico , Albuquerque , NM , 87131;2. Division of Statistics , University of California at Davis , Davis , CA , 95616 , USA |
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Abstract: | Standard methods for analyzing binomial regression data rely on asymptotic inferences. Bayesian methods can be performed using simple computations, and they apply for any sample size. We provide a relatively complete discussion of Bayesian inferences for binomial regression with emphasis on inferences for the probability of “success.” Furthermore, we illustrate diagnostic tools, perform model selection among nonnested models, and examine the sensitivity of the Bayesian methods. |
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Keywords: | Bayesian analysis Importance sampling Kullback–Leibler divergence Logistic regression Model selection Prediction Probit analysis Regression diagnostics. |
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