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 volume enables actual problems encountered by data analysts to be handled much more successfully. Amongst various topics explicitly 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.
For statisticians working in mathematics, the social, behavioural, and medical sciences, and engineering.

Inhalt

Prefaces. 1. Statistical Models and Information Criteria. 2. Variable Selection for Categorical Data. 3. CATDAP and Its Applications. 4. Bayesian Binary Regression - Univariate Case. 5. Histogram and Bayesian Density Estimator. 6. Bayesian Binary Regression - Bivariate Case. Appendix: FORTRAN Program - CATDAP-02. References. Index.