ERIC Number: EJ863073
Record Type: Journal
Publication Date: 2009-Dec
Abstractor: As Provided
Reference Count: 0
Bayesian Model Comparison for the Order Restricted RC Association Model
Iliopoulos, G.; Kateri, M.; Ntzoufras, I.
Psychometrika, v74 n4 p561-587 Dec 2009
Association models constitute an attractive alternative to the usual log-linear models for modeling the dependence between classification variables. They impose special structure on the underlying association by assigning scores on the levels of each classification variable, which can be fixed or parametric. Under the general row-column (RC) association model, both row and column scores are unknown parameters without any restriction concerning their ordinality. However, when the classification variables are ordinal, order restrictions on the scores arise naturally. Under such restrictions, we adopt an alternative parameterization and draw inferences about the equality of adjacent scores using the Bayesian approach. To achieve that, we have constructed a reversible jump Markov chain Monte Carlo algorithm for moving across models of different dimension and estimate accurately the posterior model probabilities which can be used either for model comparison or for model averaging. The proposed methodology is evaluated through a simulation study and illustrated using actual datasets.
Descriptors: Markov Processes, Classification, Bayesian Statistics, Probability, Inferences, Models, Monte Carlo Methods, Mathematics, Simulation
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Authoring Institution: N/A