The first goal of this paper is to provide a simple and general test of the presence of
asymmetric information in contractual relationships within a competitive context. We also …

Perception of inequality is important for the analysis of individuals' motivations and decisions
and for policy assessment. Despite the broad range of analytic gains that it grants, our …

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 …

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 …

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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 …

Categorical marginal models (CMMs) are flexible tools for modelling dependent or clustered
categorical data, when the dependencies themselves are not of interest. A major limitation of …

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 …

We propose an model for population size estimation in capture-recapture studies. The tb
part is based on equality constraints for the conditional capture probabilities, leading to an …

Graphical Markov models combine conditional independence constraints with graphical
representations of stepwise data generating processes. The models started to be formulated …