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.
Eskandari,F. and Meshkani,M. R. (2022). Bayesian Logistic Regression Model Choice via Laplace-Metropolis Algorithm. Journal of the Iranian Statistical Society, 5(1), 9-24.
MLA
Eskandari,F. , and Meshkani,M. R. . "Bayesian Logistic Regression Model Choice via Laplace-Metropolis Algorithm", Journal of the Iranian Statistical Society, 5, 1, 2022, 9-24.
HARVARD
Eskandari F., Meshkani M. R. (2022). 'Bayesian Logistic Regression Model Choice via Laplace-Metropolis Algorithm', Journal of the Iranian Statistical Society, 5(1), pp. 9-24.
CHICAGO
F. Eskandari and M. R. Meshkani, "Bayesian Logistic Regression Model Choice via Laplace-Metropolis Algorithm," Journal of the Iranian Statistical Society, 5 1 (2022): 9-24,
VANCOUVER
Eskandari F., Meshkani M. R. Bayesian Logistic Regression Model Choice via Laplace-Metropolis Algorithm. JIRSS, 2022; 5(1): 9-24.
Journal of the Iranian Statistical Society