  This article is concerned with the fitting of multinomial regression models
using the so-called "Poisson Trick". The work is motivated by Chen & Kuo (2001)
and Malchow-M{\o}ller & Svarer (2003) which have been criticized for being
computationally inefficient and sometimes producing nonsense results. We first
discuss the case of independent data and offer a parsimonious fitting strategy
when all covariates are categorical. We then propose a new approach for
modelling correlated responses based on an extension of the Gamma-Poisson
model, where the likelihood can be expressed in closed-form. The parameters are
estimated via an Expectation/Conditional Maximization (ECM) algorithm, which
can be implemented using functions for fitting generalized linear models
readily available in standard statistical software packages. Compared to
existing methods, our approach avoids the need to approximate the intractable
integrals and thus the inference is exact with respect to the approximating
Gamma-Poisson model. The proposed method is illustrated via a reanalysis of the
yogurt data discussed by Chen & Kuo (2001).
