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