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 …

Statistical and mathematical models are defined by parameters that describe different
characteristics of those models. Ideally it would be possible to find parameter estimates for …

Latent class (LC) analysis is used by social, behavioral, and medical science researchers
among others as a tool for clustering (or unsupervised classification) with categorical …

Binary data latent class analysis is a form of model-based clustering applied in a wide range
of fields. A central assumption of this model is that of conditional independence of responses …

Administrative data are increasingly important in statistics, but, like other types of data, may
contain measurement errors. To prevent such errors from invalidating analyses of scientific …

The empirical analysis of inequality of opportunity centres on disparities between social
types, defined by the exposure to circumstances beyond individual control. Despite this, its …

In this article, we present a method for determining whether a model is at least locally
identifiable and in the case of non-identifiable models whether any of the parameters are …

Latent class analysis (LCA) for categorical data is a model-based clustering and
classification technique applied in a wide range of fields including the social sciences …

Discrete state‐space models are used in ecology to describe the dynamics of wild animal
populations, with parameters, such as the probability of survival, being of ecological interest …

Marginal models involve restrictions on the conditional and marginal association structure of
a set of categorical variables. They generalize log-linear models for contingency tables …