The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

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 …

In this paper, we address the problem of learning the structure of a pairwise graphical model
from samples in a high-dimensional setting. Our first main result studies the sparsistency, or …

Random Forests in combination with Stability Selection allow to estimate stable conditional
independence graphs with an error control mechanism for false positive selection. This …

We study the problem of learning the graph structure associated with a general discrete
graphical models (each variable can take any of $ m> 1$ values, the clique factors have …

Contingency table analysis routinely relies on log-linear models, with latent structure
analysis providing a common alternative. Latent structure models lead to a reduced rank …

Statistical hypothesis testing is a popular and powerful tool for inferring knowledge from
data. For every such test performed, there is always a non-zero probability of making a false …

Association discovery is a fundamental data mining task. The primary statistical approach to
association discovery between variables is log-linear analysis. Classical approaches to log …

Graphical log-linear models are effective for representing complex structures that emerge
from high-dimensional data. It is challenging to fit an appropriate model in the high …

Log-linear analysis is the primary statistical approach to discovering conditional
dependencies between the variables of a dataset. A good log-linear analysis method …