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Association between categorical variables in contingency tables is analyzedusing the information identities based on multivariate multinomialdistributions. A scheme of geometric decompositions of the informationidentities is developed to identify indispensable predictors and interactioneffects in the construction of concise log-linear and logit models; it suggestsa new approach for selecting parsimonious log-linear and logit models whichwould facilitate the search for the minimum AIC models as a byproduct. Theproposed constructive schemes are illustrated along with the analysis of acontingency data table collected in a study on the risk factors of ischemiccerebral stroke.

A Constructive Procedure for Modeling Categorical Variables: Log-Linear and Logit Models