Longitudinal Categorical Data Analysis, 2014 Springer Series in Statistics Series

This is the first book in longitudinal categorical data analysis with parametric correlation models developed based on dynamic relationships among repeated categorical responses. This book is a natural generalization of the longitudinal binary data analysis to the multinomial data setup with more than two categories. Thus, unlike the existing books on cross-sectional categorical data analysis using log linear models, this book uses multinomial probability models both in cross-sectional and longitudinal setups. A theoretical foundation is provided for the analysis of univariate multinomial responses, by developing models systematically for the cases with no covariates as well as categorical covariates, both in cross-sectional and longitudinal setups. In the longitudinal setup, both stationary and non-stationary covariates are considered. These models have also been extended to the bivariate multinomial setup along with suitable covariates. For the inferences, the book uses the generalized quasi-likelihood as well as the exact likelihood approaches.

The book is technically rigorous, and, it also presents illustrations of the statistical analysis of various real life data involving univariate multinomial responses both in cross-sectional and longitudinal setups. This book is written mainly for the graduate students and researchers in statistics and social sciences, among other applied statistics research areas. However, the rest of the book, specifically the chapters from 1 to 3, may also be used for a senior undergraduate course in statistics.

Introduction.- Overview of Regression Models for Cross-sectional Univariate Categorical Data.- Regression Models for Univariate Longitudinal Stationary Categorical Data.- Regression Models for Univariate Longitudinal Non-stationary Categorical Data.- Multinomial Models for Cross-sectional Bivariate Categorical Data.- Multinomial Models for Longitudinal Bivariate Categorical Data.- Index.

Provides a comprehensive approach to analysing longitudinal data, with real life examples from the social sciences and medicine Covers univariate, bi-variate, and multivariate models for categorical analysis of longitudinal data Primary audience is researchers and graduate students in statistics and social sciences or applied statistics research areas, but chapters 1-3 could also be used in advanced undergraduate courses Includes supplementary material: sn.pub/extras