Bibliographic Information

Categorical data analysis by AIC

by Y. Sakamoto

(Mathematics and its applications, Japanese series ; v. 7)

KTK Scientific Publishers , Sold and distributed in the U.S.A. and Canada by Kluwer Academic Publishers, c1991

Other Title

Kategori karudēta no moderu bunseki

カテゴリカルデータのモデル分析

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Note

Translation of: Kategori karudēta no moderu bunseki

Includes bibliographical references (p. [207]-212) and index

Description and Table of Contents

Description

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.

Table of Contents

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.

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