Categorical Data Analysis by AIC

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This volume presents a practical and unified approach to categorical data analysis based on the Akaike Information Criterion (AIC) and the Akaike Bayesian Information Criterion (ABIC). Conventional procedures for categorical data analysis are often inappropriate because the classical test procedures employed are too closely related to specific models. The approach described in this work enables actual problems encountered by data analysts to be handled much more successfully. Amongst the topics dealt with are the problem of variable selection for categorical data, a Bayesian binary regression and a nonparametric density estimator and its application to nonparametric test problems. The practical utility of the procedure developed is demonstrated by considering its application to the analysis of various data. This volume complements the volume Akaike Information Criterion Statistics which has already appeared in this series. It should be of interest to statisticians working in mathematics, the social, behavioural and medical sciences, and engineering.