We extend Bergsma and Rudas (2002)'s hierarchical complete marginal parameterization to
allow for logits and higher order effects of global and continuation type which may be more …

Log-linear models are a classical tool for the analysis of contingency tables. In particular, the
subclass of graphical log-linear models provides a general framework for modelling …

Generalized log-linear models can be used to describe the association structure and/or the
marginal distributions of multivariate categorical responses. We simultaneously model the …

First we derive the maximal breakdown value of regression equivariant estimators in two-
way contingency tables under the loglinear independence model. We then prove that the L1 …

Given a set of discrete response variables, some of which are ordinal, and an arbitrary set of
discrete explanatory variables, we propose a simple matrix formulation for parameterising …

In generalized linear models each observation is linked with a predicted value based on a
linear function of some systematic effects. We sometimes require to link each observation …

We define a class of generalized log-linear models with random effects. For a vector of
Poisson or multinomial means m and matrices of constants C and A, the model has the form …

In this article, we propose a generalized estimating equations (GEE) approach for correlated
ordinal or nominal multinomial responses using a local odds ratios parameterization. Our …

In the context of two-way contingency tables, the RC model may be seen as a way of
modeling the set of log-odds ratios for adjacent 2× 2 subtables through row and column …

Log-linear models are the popular workhorses of analyzing contingency tables. A log-linear
parameterization of an interaction model can be more expressive than a direct …