Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology (1993)
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BibTeX
@MISC{Raftery93modelselection,
author = {Adrian E. Raftery and Sylvia Richardson},
title = {Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology},
year = {1993}
}
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Abstract
Epidemiological studies for assessing risk factors often use logistic regression, log-linear models, or other generalized linear models. They involve many decisions, including the choice and coding of risk factors and control variables. It is common practice to select independent variables using a series of significance tests and to choose the way variables are coded somewhat arbitrarily. The overall properties of such a procedure are not well understood, and conditioning on a single model ignores model uncertainty, leading to underestimation of uncertainty about quantities of interest (QUOIs). We describe a Bayesian modeling strategy that formalizes the model selection process and propagates model uncertainty through to inference about QUOIs. Each possible combination of modeling decisions defines a different model, and the models are compared using Bayes factors. Inference about a QUOI is based on an average of its posterior distributions under the individual models, weighted by thei...
Keyphrases
generalized linear model model selection risk factor epidemiological study bayes factor overall property posterior distribution log-linear model way variable many decision significance test bayesian modeling strategy independent variable different model possible combination single model ignores common practice individual model control variable model selection process logistic regression model uncertainty
