TY - JOUR
T1 - Comparisons of some bivariate regression models
AU - Famoye, Felix
N1 - Funding Information:
This work was done while Felix Famoye, Central Michigan University,was on sabbatical leave at the Department of Mathematics, University of Lagos, Nigeria. The author gratefully acknowledges the support received from the US Department of State, Bureau of Education and Cultural Affairs under the grant #09-78737. The author is grateful to the anonymous reviewers for their valuable suggestions that improved the presentation.
PY - 2012/7
Y1 - 2012/7
N2 - The bivariate negative binomial regression (BNBR) and the bivariate Poisson log-normal regression (BPLR) models have been used to describe count data that are over-dispersed. In this paper, a new bivariate generalized Poisson regression (BGPR) model is defined. An advantage of the new regression model over the BNBR and BPLR models is that the BGPR can be used to model bivariate count data with either over-dispersion or under-dispersion. In this paper, we carry out a simulation study to compare the three regression models when the true data-generating process exhibits over-dispersion. In the simulation experiment, we observe that the bivariate generalized Poisson regression model performs better than the bivariate negative binomial regression model and the BPLR model.
AB - The bivariate negative binomial regression (BNBR) and the bivariate Poisson log-normal regression (BPLR) models have been used to describe count data that are over-dispersed. In this paper, a new bivariate generalized Poisson regression (BGPR) model is defined. An advantage of the new regression model over the BNBR and BPLR models is that the BGPR can be used to model bivariate count data with either over-dispersion or under-dispersion. In this paper, we carry out a simulation study to compare the three regression models when the true data-generating process exhibits over-dispersion. In the simulation experiment, we observe that the bivariate generalized Poisson regression model performs better than the bivariate negative binomial regression model and the BPLR model.
KW - Monte Carlo simulation
KW - likelihood ratio test
KW - non-nested models
KW - over-dispersion
UR - http://www.scopus.com/inward/record.url?scp=84863594659&partnerID=8YFLogxK
U2 - 10.1080/00949655.2010.543679
DO - 10.1080/00949655.2010.543679
M3 - Article
AN - SCOPUS:84863594659
VL - 82
SP - 937
EP - 949
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
SN - 0094-9655
IS - 7
ER -