Author

  • Henry De-Graft Acquah

    • University of Cape Coast, Cape Coast, Ghana
    • Ghana

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.

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References

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How to Cite

De-Graft Acquah, H. (2013). Bayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm. Journal of Social and Development Sciences, 4(4), 193-197. https://doi.org/10.22610/jsds.v4i4.751

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Copyright (c) 2013 Henry De-Graft Acquah (Author)

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Section

Research Paper

Published in

Vol. 4 No. 4 (2013) People, Ethics & Quantitative Inquiry View issue contents

Published

2013-04-30

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