Modeling Log-linear and Logit Models in Categorical Data Analysis

01/04/2018

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by   Philip E. Cheng, et al.

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The association between categorical variables is analyzed using the mutual information approach complied with the multivariate multinomial distributions. Schematic decompositions of mutual information are employed for characterizing log-linear and logit models. A geometric analysis of the conditional mutual information is proposed for selecting indispensable predictors and their interaction effects for constructing log-linear and logit models. The new approach to selecting the most concise logit model also facilitates search for the minimum AIC model with a finite set of predictors. The proposed constructive schemes are illustrated in analyzing a contingency table of data collected in a study on the risk factors of ischemic cerebral stroke.