Abstract
In this article, a Bayesian model averaging approach for hier archical log-linear models is considered. Posterior model probabilities are approximately calculated for hierarchical log-linear models. Dimension of interested model space is reduced by using Occam’s window and Occam’s razor approaches. 2002 road traffic accident data of Turkey is analyzed by using the considered approach
Highlights
Many fields of scientific investigation include analysis of qualitative data
The association structure can be displayed by a Bayesian approach of log-linear modelling
The Bayesian approach is more advantageous than the classical setting because the inference is exact rather than asymptotic
Summary
Many fields of scientific investigation include analysis of qualitative data. It is important to discover association structure of the interested categorical variables. General advantages of the Bayesian approach are valid here In this sense, prior information can be induced on the log-linear parameters, expected cell counts or cell probabilities. From the Bayesian point of view, this averaging is applied such that posterior distribution of considered quantity is obtained over the set of suitable models, they are weighted by their posterior model probabilities (Leamer, 1978; Raftery, 1996). One can estimate other quantities and model parameters simultaneously, obtain entire posterior distributions of them in the Bayesian setting. Standard error estimates of the parameters and the considered quantities will be smaller, and one can draw inferences on the distribution of model parameters These are significant gains of the BMA approach in general.
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