Authors

• Farzad Eskandari
• M. Reza Meshkani

Abstract

Following a Bayesian statistical inference paradigm, we
provide an alternative methodology for analyzing a multivariate logistic
regression. We use a multivariate normal prior in the Bayesian
analysis. We present a unique Bayes estimator associated with a prior
which is admissible. The Bayes estimators of the coefficients of the
model are obtained via MCMC methods. The proposed procedure
is illustrated by analyzing a data set which has previously b"'en analyzed
by various authors. It is shown that our model is more precise
and computationally less taxing.

Keywords

• Bayes
• bayesian model selection
• Laplace--Metropolis algorithm
• Logistic regression
• multinomial distribution