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 |
Files in This Item:
File | Description | Size | Format | |
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Bayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm.pdf | Article | 703.9 kB | Adobe PDF | View/Open |
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