Drawing on the authors' extensive research in the analysis of categorical longitudinal data,
this book focuses on the formulation of latent Markov models and the practical use of these …

We provide a comprehensive overview of latent Markov (LM) models for the analysis of
longitudinal categorical data. We illustrate the general version of the LM model which …

Latent Markov (LM) models represent an important class of models for the analysis of
longitudinal data, especially when response variables are categorical. These models have a …

This chapter deals with longitudinal categorical data. Such data may be of two types, in
general. Type I refers to the situation in which a few subjects are measured repeatedly at …

This article presents a general framework for the analysis of discrete-time longitudinal data
using latent class models. The encompassing model is the mixture latent Markov model, a …

Marginal generalized linear models are now frequently used for the analysis of longitudinal
data. Semiparametric inference for marginal models was introduced by Liang and Zeger …

This Handbook covers latent variable models, which are a flexible class of models for
modeling multivariate data to explore relationships among observed and latent variables …

The process of change in a discrete characteristic is seldom the same for every respondent
in a panel survey. Mixed Markov latent class models describe cross-tables of consecutive …

The focus of this article is on Markov models for the analysis of panel data and, more
specifically, on data obtained from repeated measurements of one categorical variable at …

For the analysis of multivariate categorical longitudinal data, we propose an extension of the
dynamic logit model. The resulting model is based on a marginal parameterization of the …