We present motivation and new commands for modeling count data. While our focus is to present new commands for estimating count data, we also discuss generalized binomial regression and present the zero-inflated versions of each model.
CameronA. C., and TrivediP. K.2013. Regression Analysis of Count Data. 2nd ed. Cambridge: Cambridge University Press.
2.
ConsulP. C., and GuptaH. C.1980. The generalized negative binomial distribution and its characterization by zero regression. SIAM Journal on Applied Mathematics39: 231–237.
3.
DeanC. B.1992. Testing for overdispersion in Poisson and binomial regression models. Journal of the American Statistical Association87: 451–457.
4.
DesmaraisB. A., and HardenJ. J.2013. Testing for zero inflation in count models: Bias correction for the Vuong test. Stata Journal13: 810–835.
HilbeJ. M.2014. Modeling Count Data.Cambridge: Cambridge University Press.
10.
IrwinJ. O.1968. The generalized Waring distribution applied to accident theory. Journal of the Royal Statistical Society Series A 131: 205–225.
11.
JainG. C., and ConsulP. C.1971. A generalized negative binomial distribution. SIAM Journal on Applied Mathematics21: 501–513.
12.
Rodríguez-AviJ., Conde-SánchezA., Sáez-CastilloA. J., Olmo-JiménezM. J., and Martínez-RodríguezA. M.2009. A generalized Waring regression model for count data. Computational Statistics and Data Analysis53: 3717–3725.
13.
TangW., HeH., and TuX. M.2012. Applied Categorical and Count Data Analysis.Boca Raton, FL: Chapman & Hall/CRC.
14.
VuongQ. H.1989. Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica57: 307–333.
15.
WangX.-F., JiangZ., DalyJ. J., and YueG. H.2012. A generalized regression model for region of interest analysis of fMRI data. Neuroimage59: 502–510.
16.
WinkelmannR.2008. Econometric Analysis of Count Data. 5th ed. Berlin: Springer.
17.
YangZ., HardinJ. W., AddyC. L., and VuongQ. H.2007. Testing approaches for overdispersion in Poisson regression versus the generalized Poisson model. Biometrical Journal49: 565–584.
