Comparisons of some bivariate regression models

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Abstract

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.

Original languageEnglish
Pages (from-to)937-949
Number of pages13
JournalJournal of Statistical Computation and Simulation
Volume82
Issue number7
DOIs
StatePublished - Jul 2012

Keywords

  • Monte Carlo simulation
  • likelihood ratio test
  • non-nested models
  • over-dispersion

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