Bootstrap Model Selection in Generalized Linear Models
نویسنده
چکیده
Model selection is a central component of data analysis Though there are a variety of methods for likelihood based estimation methods there are relatively few for non likelihood based generalized linear models GLM such as in the quasi likelihood and generalized es timating equation GEE approaches In this paper we develop basic and bias corrected bootstrap approaches to estimate the predictive mean squared error PMSE of a model and use the PMSE for model selection Simulation studies show that the bias corrected boot strap estimate works well when quasi likelihood or GEE is used to t either overdispersed or correlated response GLMs For correlated response data when the marginal distribution assumption is almost correct Akaike s Information Criterion AIC and Bayesian Infor mation Criterion BIC calculated under the working independence model also perform well For illustration the methods are applied to data sets from evolutionary biology and teratology
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