The focus of this book is on models for contingency table analysis. It deals mainly with log-
linear models and special models for ordinal data (log-linear or nonlinear). Special models …

This book offers a fairly self-contained account of the fundamental results in categorical data
analysis. The somewhat old fashioned title (Lectures...) refers to the fact that the selection of …

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 …

We discuss a class of chain graph models for categorical variables defined by what we call a
multivariate regression chain graph Markov property. First, the set of local independencies of …

Marginal log-linear (MLL) models provide a flexible approach to multivariate discrete data.
MLL parameterizations under linear constraints induce a wide variety of models, including …

For advanced students of network data science, this compact account covers both well-
established methodology and the theory of models recently introduced in the graphical …

This paper introduces a novel class of models for binary data, which we call log-mean linear
models. They are specified by linear constraints on the log-mean linear parameter, defined …

In responding to a rating question, an individual may give answers either according to
his/her knowledge/awareness or to his/her level of indecision/uncertainty, typically driven by …

This paper proposes two fast methods to design network topologies with high connectivity
and survivability based on circulant graph theory. The first method, namely, the Combination …

The graphical models (GM) for categorical data are models useful to representing
conditional independencies through graphs. The parametric marginal models for categorical …