Statistical Modelling 9 (2009), 215–233
Multinomial-Poisson models subject to inequality constraints
Manuela Cazzaro
Dipartimento di Metodi Quantitativi per le Scienze Economiche ed Aziendali
Università di Milano-Bicocca
Italy
Roberto Colombi
Dipartimento di Ingegneria dell’Informazione e Metodi Matematici
Università di Bergamo
Viale Marconi, 5
I–24044 Bergamo
Italy
eMail: colombi@unibg.it
Abstract:
Lang’s Multinomial-Poisson Homogeneous (MPH) models and Homogeneous
Linear Predictor (HLP) Multinomial-Poisson models include as special
cases many models for contingency table analysis that have been
introduced in the effort to overcome well-known limitations of the
log-linear models. Here the definitions of MPH and HLP models are
extended to include inequality constraints. It is shown that
inequality constrained MPH and HLP models are very flexible and
rich family of models for contingency table analysis. The
inequality constrained hierarchical multinomial marginal models
which are an important sub-class of MPH models are also examined.
Keywords:
constrained statistical inference; generalized odds ratios;
marginal models; multinomial-Poisson homogeneous models;
multivariate logit models
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