Source: Braz. J. Probab. Stat. Volume 24, Number 1
(2010), 91-99.
In this article we propose a generalized negative binomial distribution, which is constructed based on an extended Poisson process (a generalization of the homogeneous Poisson process). This distribution is intended to model discrete data with presence of zero-inflation and over-dispersion. For a dataset on animal abundance which presents over-dispersion and a high frequency of zeros, a comparison between our extended distribution and other common distributions used for modeling this kind of data is addressed, supporting the fitting of the proposed model.
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References
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In, Second International Symposium on Information Theory 267–281. Akadémia Kiadó, Budapest.
Bliss, C. I. and Fisher, R. A. (1953). Fitting the negative binomial distribution to biological data., Biometrics 9 176–200.
Mathematical Reviews (MathSciNet):
MR55625
Cox, D. R. and Miller, H. D. (1965)., The Theory of Stochastic Processes. Wiley, New York.
Mathematical Reviews (MathSciNet):
MR192521
Faddy, M. J. (1997). Extended Poisson process modelling and analysis of count data., Biometrical Journal 39 431–440.
Feller, W. (1971)., An Introduction to Probability Theory and Applications, Vol. I, 3rd ed. Wiley, New York.
Mathematical Reviews (MathSciNet):
MR270403
Greenwood, M. and Yule, G. U. (1920). An inquiry into the nature of frequency distributions representative of multiple happening with special reference of multiple attacks of disease or repeated accidents., Journal of the Royal Statistical Society 83 255–279.
Lewsey, J. D. and Thomson, W. M. (2004). The utility of the zero-inflated Poisson and zero-inflated negative binomial models: A case study of cross-sectional and longitudinal DMF data examining the effect of socio-economic status., Community Dentistry and Oral Epidemiology 32 183–189.
R Development Core Team (2007)., R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Available at http://www.R-project.org.
Ridout, M., Demétrio, C. G. B. and Hinde, J. (1998). Models for count data with many zeros. In, Proceedings of the XIXth International Biometric Conference 179–192. Cape Town, South Africa.
Ridout, M., Hinde, J. and Demétrio, C. G. B. (2001). A score test for testing a zero-inflated Poisson regression model against zero-inflated negative binomial alternatives., Biometrics 57 219–223.
Schwarz, G. (1978). Estimating the dimension of a model., Annals of Statistics 6 461–464.
Mathematical Reviews (MathSciNet):
MR468014
Yau, K. K. W., Wang, K. and Lee, A. H. (2003). Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros., Biometrical Journal 45 437–452.