Bayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm

Henry De-Graft Acquah

Journal of Social and Development Sciences, 2013, vol. 4, issue 4, 193-197

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 model.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:rnd:arjsds:v:4:y:2013:i:4:p:193-197

DOI: 10.22610/jsds.v4i4.751

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