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.
