1. Introduction 2. Binary regression: the logit model 3. Generalized linear models 4. Modeling of binary data 5. Alternative binary regression models 6. Regularization and variable selection for parametric models 7. Regression analysis of count data 8. Multinomial response models 9. Ordinal response models 10. Semi- and nonparametric generalized regression 11. Tree-based methods 12. The analysis of contingency tables: log-linear and graphical models 13. Multivariate response models 14. Random effects models 15. Prediction and classification Appendix A. Distributions Appendix B. Some basic tools Appendix C. Constrained estimation Appendix D. Kullback-Leibler distance and information-based criteria of model fit Appendix E. Numerical integration and tools for random effects modeling.

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Regression for Categorical Data

Measures, Models, and Graphical Displays in the Analysis of Cross-Classified Data

A survey is given of the main strategies for modeling cross-classifications that contain ordinal variables. Models are described for two-way tables in which one or both classifications are ordered and for multidimensional tables in which at least one classification is ordered. Primary emphasis is given to construction, interpretation, and implications of loglinear and logit models.

A Survey of Strategies for Modeling Cross-Classifications Having Ordinal Variables

The present work describes a family of models that can be used for the analysis of association in multiway cross-classifications. The models are complementary to the usual (i.e., the so-called hierarchical) log-linear models. An advantage of this family of models is that it is frequently possible to give a more parsimonious description of the association than is possible with the usual log-linear models approach. The models will be especially useful when some or all of the variables are ordinal. The models are derived from a reparameterization of the saturated model based on singular value decompositions of interaction terms in the log-linear parameterization. Multivariate association models given in Goodman (1979, 1986), Clogg (1982a,b), Agresti and Kezouh (1983), Gilula and Haberman (1988), and Becker and Clogg (1989) are obtained as special cases. The utility of the models developed in this article is demonstrated with the analysis of two examples. The first example, a cross-classification of ...

Models for the Analysis of Association in Multivariate Contingency Tables

On the bivariate normal distribution and association models for ordinal categorical data

Models are formulated for describing associations among ordinal variables in multidimensional tables.Uniform association and uniform interaction models occur as special cases in which equal-interval scores are assigned to levels of the variables.The models described are extensions of ones proposed by Goodman (1979).

Abbas Kezouh

Papers

Association Models for Multi-Dimensional Cross-Classifications of Ordinal Variables