Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models

Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models (1993)

by Adrian E. Raftery

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Datum	Value	Source
TITLE	Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models	user correction
AUTHOR NAME	Adrian E. Raftery	user correction
AUTHOR AFFIL	Department of Statistics, GN·22; University of Washington; Seattle, Washington 98195 USAApproximate Bayes Factors and Accounting for	user correction
ABSTRACT	Ways of obtaining approximate Bayes factors for generalized linear models are described, based on the Laplace method for integrals. I propose a new approximation which uses only the output of standard computer programs such as GUM; this appears to be quite accurate. A reference set of proper priors is suggested, both to represent the situation where there is not much prior information, and to assess the sensitivity of the results to the prior distribution. The methods can be used when the dispersion parameter is unknown, when there is overdispersion, to compare link functions, and to compare error distributions and variance functions. The methods can be used to implement the Bayesian approach to accounting for model uncertainty. I describe an application to inference about relative risks in the presence of control factors where model uncertainty is large and important. Software to implement the	SVM HeaderParse 0.2
YEAR	1993	user correction
CITATIONS	39 found	ParsCit 1.0