AN APPLICATION OF THE BAYESIAN POISSON REGRESSION IN MODELLING ROOMMATE CONFLICT AMONG UNIVERSITY OF CAPE COAST STUDENTS

Résumé

This paper introduces Bayesian analysis and demonstrates its application to parameter estimation of the Poisson regression via Markov Chain Monte Carlo (MCMC) algorithm using roommate conflict data. The Bayesian Poisson regression estimation is compared with the classical Poisson regression. Both the classical Poisson regression and the Bayesian Poisson regression provide similar results and suggest that the frequency of roommate conflicts decreases with family size, number of roommates one has and being in a love relationship. The results also show a reduction of standard errors associated with some coefficients obtained from the Bayesian analysis, thus bringing greater stability to the coefficients. It is concluded that Bayesian Poisson regression estimation via MCMC algorithm offers an alternative framework for modelling roommate conflict data.

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