Volume 5, Number 1 and 2 (November 2006)                   JIRSS 2006, 5(1 and 2): 9-24 | Back to browse issues page


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Eskandari F, Meshkani M R. Bayesian Logistic Regression Model Choice via Laplace-Metropolis Algorithm. JIRSS. 2006; 5 (1 and 2) :9-24
URL: http://jirss.irstat.ir/article-1-133-en.html

Abstract:   (11138 Views)
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.
Full-Text [PDF 220 kb]   (2889 Downloads)    
Subject: 60: Probability theory and stochastic processes
Received: 2011/11/4 | Accepted: 2015/09/12

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