Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/4290
Title: Bayesian logistic regression modelling via markov chain monte carlo algorithm
Authors: Acquah, Henry de-Graft
Keywords: Logistic regression
Posterior Distribution
Markov Chain Monte Carlo
Openness of Trade
Bayesian Analysis
Issue Date: Apr-2013
Publisher: University of Cape Coast
Abstract: This paper introduces Bayesian analysis and demonstrates its application to parameter estimation of the logistic regression via Markov Chain Monte Carlo (MCMC) algorithm. The Bayesian logistic regression estimation is compared with the classical logistic regression. Both the classical logistic regression and the Bayesian logistic regression suggest that higher per capita income is associated with free trade of countries. The results also show a reduction of standard errors associated with the coefficients obtained from the Bayesian analysis, thus bringing greater stability to the coefficients. It is concluded that Bayesian Markov Chain Monte Carlo algorithm offers an alternative framework for estimating the logistic regression mode
Description: 197p:, ill.
URI: http://hdl.handle.net/123456789/4290
ISSN: 23105496
Appears in Collections:Department of Agricultural Economics & Extension

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