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References

Published online by Cambridge University Press:  05 June 2012

Joseph M. Hilbe
Joseph M. Hilbe
Affiliation:
Arizona State University
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Negative Binomial Regression , pp. 242 - 246
Publisher: Cambridge University Press
Print publication year: 2007

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References

Allison, P. D. and Waterman, R. (2002). Fixed-effects negative binomial regression models, unpublished manuscript.Google Scholar
Amemiya, T. (1984). Tobit models: a survey, Journal of Econometrics 24: 3–61.CrossRefGoogle Scholar
Anscombe, F. J. (1948). The transformations of Poisson, binomial, and negative binomial data, Biometrika 35: 246–254.CrossRefGoogle Scholar
Anscombe, F. J. (1949). The statistical analysis of insect counts based on the negative binomial distribution, Biometrics 5: 165–173.CrossRefGoogle ScholarPubMed
Anscombe, F. J. (1972). Contribution to the discussion of H. Hotelling's paper, Journal of the Royal Statistical Society – Series B 15(1): 229–230.Google Scholar
Bartlett, M. S. (1947). The use of transformations, Biometrics 3: 39–52.CrossRefGoogle ScholarPubMed
Beall, G. (1942). The transformation of data from entomological field experiments so that that analysis of variance becomes applicable, Biometrika 29: 243–262.CrossRefGoogle Scholar
Blom, G. (1954). Transformations of the binomial, negative binomial, Poisson, and χ2 distributions, Biometrika 41: 302–316.Google Scholar
Breslow, N. E. (1984). Extra-Poisson variation in log-linear models, Applied Statistics 33(1): 38–44.CrossRefGoogle Scholar
Breslow, N. (1985). Cohort analysis in epidemiology, in Celebration of Statistics, ed. Atkinson, A. C. and Fienberg, S. E., New York: Springer-Verlag.Google Scholar
Cameron, A. C. and Trivedi, P. K. (1986). Econometric models based on count data: comparisons and applications of some estimators, Journal of Applied Econometrics 1: 29–53.CrossRefGoogle Scholar
Cameron, A. C. and Trivedi, P. K. (1990). Regression-based tests for overdispersion in the Poisson model, Journal of Econometrics 46: 347–364.CrossRefGoogle Scholar
Cameron, A. C. and Trivedi, P. K. (1998). Regression Analysis of Count Data, New York: Cambridge University Press.CrossRefGoogle Scholar
Collett, D. (1989). Modelling Binary Data, London: Chapman & Hall.Google Scholar
Consul, P. and Jain, G. (1973). A generalization of the Poisson distribution, Technometrics 15: 791–799.CrossRefGoogle Scholar
Consul, P. C. and Gupta, R. C. (1980). The generalized binomial distribution and its characterization by zero regression, SIAM Journal of Applied Mathematics 39(2): 231–237.CrossRefGoogle Scholar
Consul, P. and Famoye, F. (1992). Generalized Poisson regression model, Communications in Statistics – Theory and Method 21: 89–109.CrossRefGoogle Scholar
Drescher, D. (2005). Alternative distributions for observation driven count series models, Economics Working Paper No. 2005–11, Christian-Albrechts-Universitat, Kiel, Germany.Google Scholar
Edwards, A. W. F. (1972). Likelihood, Baltimore, MD: John Hopkins University Press.Google Scholar
, Eggenberger F. and Polya, G. (1923). Über die Statistik Verketteter Vorgänge, Journal of Applied Mathematics and Mechanics 1: 279–289.Google Scholar
Englin, J. and Shonkwiler, J. (1995). Estimating social welfare using count data models: an application under conditions of endogenous stratification and truncation, Review of Economics and Statistics 77: 104–112.CrossRefGoogle Scholar
Fair, R. (1978). A theory of extramarital affairs, Journal of Political Economy 86: 45–61.CrossRefGoogle Scholar
Famoye, F. and Singh, K. (2006). Zero-truncated generalized Poisson regression model with an application to domestic violence, Journal of Data Science 4: 117–130.Google Scholar
Famoye, F (1995). Generalized binomial regression model, Biometrical Journal 37(5): 581–594.CrossRefGoogle Scholar
Faraway, J. (2006). Extending the Linear Model with R, Boca Raton, FL: Chapman & Hall/CRC Press.Google Scholar
Frees, E. (2004). Longitudinal and Panel Data, Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Goldberger, A. S. (1983). Abnormal selection bias, in Studies in Econometrics, Time Series, and Multivariate Statistics, ed. Karlin, S., Amemiya, T., and Goodman, L. A., New York: Academic Press, pp. 67–85.Google Scholar
Gould, W., Pitblado, J., and Sribney, W. (2006). Maximum Likelihood Estimation with Stata, Third Edition, College Station: Stata Press.Google Scholar
Greene, W. H. (1992). Statistical models for credit scoring, Working Paper, Department of Economics, Stern School of Business, New York University.Google Scholar
Greene, W. H. (1994). Accounting for excess zeros and sample selection in Poisson and negative binomial regression models, EC-94–10, Department of Economics, Stern School of Business, New York University.Google Scholar
Greene, W. H. (2003). Econometric Analysis, Fifth Edition, New York: Macmillan.Google Scholar
Greene, W. H. (2006). LIMDEP Econometric Modeling Guide, Version 9, Plainview, NY: Econometric Software Inc.Google Scholar
Greene, W. H. (2006a). A general approach to incorporating ‘selectivity’ in a model, Working Paper, Department of Economics, Stern School of Business, New York University.Google Scholar
Greenwood, M. and Yule, G. U. (1920) An inquiry into the nature of frequency distributions of multiple happenings, with particular reference to the occurrence of multiple attacks of disease or repeated accidents, Journal of the Royal Statistical Society A, 83: 255–279.CrossRefGoogle Scholar
Gurmu, S. and Trivedi, P. K. (1992). Overdispersion tests for truncated Poisson regression models, Journal of Econometrics 54: 347–370.CrossRefGoogle Scholar
Hardin, J. W. and Hilbe, J. M. (2001). Generalized Linear Models and Extensions, College Station, TX: Stata PressGoogle Scholar
Hardin, J. W. and Hilbe, J. M. (2002). Generalized Estimating Equations, Boca Raton, FL: Chapman & Hall/CRC Press.CrossRefGoogle Scholar
Hardin, J. W. and Hilbe, J. M. (2007). Generalized Linear Models and Extensions, Second Edition, College Station, TX: Stata PressGoogle Scholar
Hardin, J. W. (2003). The sandwich estimate of variance, in Advances in Econometrics: Maximum Likelihood of Misspecified Models: Twenty Years Later, ed. Fomby, T. and Hill, C., Elsevier, 17: 45–73.Google Scholar
Hausman, J., Hall, B., and Griliches, Z. (1984). Econometric models for count data with an application to the patents – R&D relationship, Econometrica 52: 909–938.CrossRefGoogle Scholar
Heckman, J. (1979). Sample selection bias as a specification error, Econometrica, 47: 153–161.CrossRefGoogle Scholar
Heilbron, D. (1989). Generalized linear models for altered zero probabilities and overdispersion in count data, Technical Report, Department of Epidemiology and Biostatistics, University of California, San Francisco.Google Scholar
Hilbe, J. M. (1993a). Log negative binomial regression as a generalized linear Model, technical Report COS 93/945–26, Department of Sociology, Arizona State University.Google Scholar
Hilbe, J. (1993b). Generalized linear models, Stata Technical Bulletin, STB-11, sg16.Google Scholar
Hilbe, J. (1993c). Generalized linear models using power links, Stata Technical Bulletin, STB-12, sg16.1Google Scholar
Hilbe, J. (1994a). Negative binomial regression, Stata Technical Bulletin, STB-18, sg16.5Google Scholar
Hilbe, J. (1994b). Generalized linear models, The American Statistician, 48(3): 255–265.Google Scholar
Hilbe, J. (2000). Two-parameter log-gamma and log-inverse Gaussian models, in Stata Technical Bulletin Reprints, College Station, TX: Stata Press, pp. 118–121.Google Scholar
Hilbe, J. (2005a). CPOISSON: Stata module to estimate censored Poisson regression, Boston College of Economics, Statistical Software Components, http://ideas.repec.org/c/boc/bocode/s456411.htmlGoogle Scholar
Hilbe, J. (2005b), CENSORNB: Stata module to estimate censored negative binomial regression as survival model, Boston College of Economics, Statistical Software Components, http://ideas.repec.org/c/boc/bocode/s456508.htmlGoogle Scholar
Hilbe, J. and Turlach, B. (1995). Generalized linear models, in XploRe: An Interactive Statistical Computing Environment, ed. Hardle, W., Klinke, S., and Tulach, B., New York: Springer-Verlag, pp. 195–222.Google Scholar
Hilbe, J. (1998). Right, left, and uncensored Poisson regression, Statistical Bulletin, 46: 18–20.Google Scholar
Hilbe, J. and Greene, W. (2007). Count response regression models, in Epidemiology and Medical Statistics, ed. Rao, C. R., Miller, J. P, and Rao, D. C., Elsevier Handbook of Statistics Series, London, UK: Elsevier.Google Scholar
Hoffmann, J. (2004). Generalized Linear Models, Boston, MA: Allyn & Bacon.Google Scholar
Hosmer, D. and Lemeshow, S. (2003). Applied Logistic Regression, second edition, New York: WileyGoogle Scholar
Huber, P. J. (1967). The behavior of maximum likelihood estimates under nonstandard conditions, in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 221–233.Google Scholar
Ihaka, R. and Gentleman, R. (1996). “R: A language for data analysis and graphics,” Journal of Computational and Graphical Statistics 5: 299–314.Google Scholar
Jain, G. C. and Consul, P. C. (1971). A generalized negative binomial distibution, SIAM Journal of Applied Mathematics 21(4): 501–513.CrossRefGoogle Scholar
Johnston, G. (1998). SAS/STAT/GENMOD procedure, SAS InstituteGoogle Scholar
Karim, M. R. and Zeger, S. (1989). A SAS macro for longitudinal data analysis, Department of Biostatistics, Technical Report 674, The John Hopkins University.Google Scholar
Katz, E. (2001). Bias in conditional and unconditional fixed effects logit estimation, Political Analysis 9(4): 379–384.CrossRefGoogle Scholar
King, G. (1988). Statistical models for political science event counts: bias in conventional procedures and evidence for the exponential Poisson regression model, American Journal of Political Science 32: 838–863.CrossRefGoogle Scholar
King, G. (1989). Event count models for international relations: generalizations and applications, International Studies Quarterly 33: 123–147.CrossRefGoogle Scholar
Lambert, D. (1992). Zero-inflated Poisson regression with an application to defects in manufacturing, Technometrics 34: 1–14.CrossRefGoogle Scholar
Lancaster, T. (2002). Orthogonal parameter and panel data, Review of Economic Studies 69: 647–666.CrossRefGoogle Scholar
Lawless, J. F. (1987). Negative binomial and mixed Poisson regression, Canadian Journal of Statistics 15, (3): 209–225.CrossRefGoogle Scholar
Lee, Y., Nelder, J., and Pawitan, Y. (2006). Generalized Linear Models with Random Effects, Boca Raton, FL: Chapman & Hall/CRC Press.CrossRefGoogle Scholar
Liang, K.-Y. and Zeger, S. (1986). Longitudinal data analysis using generalized linear models, Biometrika, 73: 13–22.CrossRefGoogle Scholar
Long, J. S. (1997). Regression Models for Categorical and Limited Dependent Variables, Thousand Oaks, CA: Sage.Google Scholar
Long, J. S. and Freese, J. (2003, 2006). Regression Models for Categorical Dependent Variables using Stata, Second Edition, College Station, TX: Stata Press.Google Scholar
Loomis, J. B. (2003). Travel cost demand model based river recreation benefit estimates with on-site and household surveys: comparative results and a correction procedure, Water Resources Research 39(4): 1105.CrossRefGoogle Scholar
Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters, Journal of the Society for Industrial and Applied Mathematics 11: 431–441.CrossRefGoogle Scholar
Martinez-Espiñeira, R., Amoako-Tuffour, J., and Hilbe, J. M. (2006), Travel cost demand model based river recreation benefit estimates with on site and household surveys: comparative results and a correction procedure – revaluation, Water Resource Research, 42.CrossRefGoogle Scholar
McCullagh, P. (1983). Quasi-likelihood functions, Annals of Statistics 11: 59–67.CrossRefGoogle Scholar
McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, Second Edition, New York: Chapman & Hall.CrossRefGoogle Scholar
Mullahy, J. (1986). Specification and testing of some modified count data models, Journal of Econometrics 33: 341–365.CrossRefGoogle Scholar
Mundlak, Y. (1978). On the pooling of time series and cross section data, Econometrica 46: 69–85.CrossRefGoogle Scholar
Mwalili, S., Lesaffre, E., and DeClerk, D. (2005). The zero-inflated negative binomial regresson model with correction for misclassification: an example in Caries Research, Technical Report 0462, LAP Statistics Network Interuniversity Attraction Pole, Catholic University of Louvain la Neuve, Belgium, www.stat.ucl.ac.be/IAP.Google Scholar
Nelder, J. A. (1994). Generalized linear models with negative binomial or beta-binomial errors, unpublished manuscript.Google Scholar
Nelder, J. A. and Wedderburn, R. W. M. (1972). Generalized linear models, Journal of the Royal Statistical Society, Series A, 135(3): 370–384.CrossRefGoogle Scholar
Nelder, J. and Pregibon, D. (1987). An extended quasi-likelihood function, Biometrika 74: 221–232.CrossRefGoogle Scholar
Nelder, J. A. and Lee, Y. (1992). Likelihood, quasi-likelihood, and pseudo-likelihood: some comparisons, Journal of the Royal Statistical Society, Series B, 54: 273–284.Google Scholar
Nelson, D. L. (1975). Some remarks on generalized of negative binomial and Poisson distributions, Technometrics 17: 135–136.CrossRefGoogle Scholar
Neyman, O, and Scott, E. L. (1948). Consistent estimation from partially consistent observations, Econometrica 16(1): 1–32.CrossRefGoogle Scholar
Pan, W. (2001). Akaike's information criterion in generalized estimating equations, Biometrics 57: 120–125.CrossRefGoogle ScholarPubMed
Pan, W. (2001a). On the robust variance estimator in generalized estimating equations, Biometrika 88(3): 901–906.CrossRefGoogle Scholar
Pierce, D. A. and Schafer, D. W. (1986). “Residuals in Generalized Linear Models,” Journal of the American Statistical Association, 81: 977–986.CrossRefGoogle Scholar
Plackett, R. L. (1981). The Analysis of Categorical Data (2nd ed.), London: Chapman and Hall.Google Scholar
Rabe-Hesketh, S. and Skrondal, A. (2004). Generalized Latent Variable Modeling, Boca Raton, FL: Chapman & Hall/CRC Press.Google Scholar
Rabe-Hesketh, S. and Skrondal, A. (2005). Multilevel and Longitudinal Modeling Using Stata, College Station, TX: Stata Press.Google Scholar
Sikkel, D. and Jelierse, G. (1998). Renewal theory and retrospective questions, Applied Statistics 37: 312–420.Google Scholar
Shaw, D. (1988). On-site samples' regression, Journal of Econometrics 37: 211–223.CrossRefGoogle Scholar
Terza, J. V. (1998). A Tobit-type estimator for the censored Poisson regression model, Econometric Letters 18: 361–365.CrossRefGoogle Scholar
Thall, P. and Vail, S. (1990). Some covariance models for longitudinal count data and overdispersion, Biometrika 46: 657–671.CrossRefGoogle ScholarPubMed
Twisk, J. W. R. (2003). Applied Longitudinal Data Analysis for Epidemiology, Cambridge: Cambridge University Press.Google Scholar
Vadeby, A. (2002). Estimation in a model with incidental parameters, LiTH-MAT-R-2002-02 working paper.Google Scholar
Venables, W. and Ripley, B. (2002). Modern Applied Statistics with S, Fourth Edition, New York: Springer-Verlag.CrossRefGoogle Scholar
Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses, Econometrica 57: 307–333.CrossRefGoogle Scholar
Wedderburn, R. W. M. (1974). Quasi-likelihood functions, generalized linear models and the Gauss–Newton method. Biometrika 61: 439–447.Google Scholar
, White H. (1980). A heteroskestasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity, Econometrica 48(4): 817–838.CrossRefGoogle Scholar
Winkelmann, R (1995). Duration dependence and dispersion in count-data models, Journal of Business and Economic Statistics 13: 467–474.Google Scholar
Winkelmann, R. (2003). Econometric Analysis of Count Data, Fourth Edition, Heidelberg, Germany: Springer-Verlag.CrossRefGoogle Scholar
Winkelmann, R., and Zimmermann, K. F. (1995). Recent developments in count data modelling: theory and application, Journal of Economic Surveys, 9: 1–24.CrossRefGoogle Scholar
Woutersen, T. (2002). Robustness against incidental parameters, Department of Economics Working paper 2008, University of Western Ontario.Google Scholar
Yang, Z., Hardin, J., and Abby, C. (2006). A score test for overdispersion based on generalized Poisson model, unpublished manuscript.Google Scholar
Zeger, S. L., Liang, K.-Y., and Albert, P. S. (1988). Models for longitudinal data: A generalized estimating equation approach, Biometrics 44: 1049–1060.CrossRefGoogle ScholarPubMed
Allison, P. D. and Waterman, R. (2002). Fixed-effects negative binomial regression models, unpublished manuscript.Google Scholar
Amemiya, T. (1984). Tobit models: a survey, Journal of Econometrics 24: 3–61.CrossRefGoogle Scholar
Anscombe, F. J. (1948). The transformations of Poisson, binomial, and negative binomial data, Biometrika 35: 246–254.CrossRefGoogle Scholar
Anscombe, F. J. (1949). The statistical analysis of insect counts based on the negative binomial distribution, Biometrics 5: 165–173.CrossRefGoogle ScholarPubMed
Anscombe, F. J. (1972). Contribution to the discussion of H. Hotelling's paper, Journal of the Royal Statistical Society – Series B 15(1): 229–230.Google Scholar
Bartlett, M. S. (1947). The use of transformations, Biometrics 3: 39–52.CrossRefGoogle ScholarPubMed
Beall, G. (1942). The transformation of data from entomological field experiments so that that analysis of variance becomes applicable, Biometrika 29: 243–262.CrossRefGoogle Scholar
Blom, G. (1954). Transformations of the binomial, negative binomial, Poisson, and χ2 distributions, Biometrika 41: 302–316.Google Scholar
Breslow, N. E. (1984). Extra-Poisson variation in log-linear models, Applied Statistics 33(1): 38–44.CrossRefGoogle Scholar
Breslow, N. (1985). Cohort analysis in epidemiology, in Celebration of Statistics, ed. Atkinson, A. C. and Fienberg, S. E., New York: Springer-Verlag.Google Scholar
Cameron, A. C. and Trivedi, P. K. (1986). Econometric models based on count data: comparisons and applications of some estimators, Journal of Applied Econometrics 1: 29–53.CrossRefGoogle Scholar
Cameron, A. C. and Trivedi, P. K. (1990). Regression-based tests for overdispersion in the Poisson model, Journal of Econometrics 46: 347–364.CrossRefGoogle Scholar
Cameron, A. C. and Trivedi, P. K. (1998). Regression Analysis of Count Data, New York: Cambridge University Press.CrossRefGoogle Scholar
Collett, D. (1989). Modelling Binary Data, London: Chapman & Hall.Google Scholar
Consul, P. and Jain, G. (1973). A generalization of the Poisson distribution, Technometrics 15: 791–799.CrossRefGoogle Scholar
Consul, P. C. and Gupta, R. C. (1980). The generalized binomial distribution and its characterization by zero regression, SIAM Journal of Applied Mathematics 39(2): 231–237.CrossRefGoogle Scholar
Consul, P. and Famoye, F. (1992). Generalized Poisson regression model, Communications in Statistics – Theory and Method 21: 89–109.CrossRefGoogle Scholar
Drescher, D. (2005). Alternative distributions for observation driven count series models, Economics Working Paper No. 2005–11, Christian-Albrechts-Universitat, Kiel, Germany.Google Scholar
Edwards, A. W. F. (1972). Likelihood, Baltimore, MD: John Hopkins University Press.Google Scholar
, Eggenberger F. and Polya, G. (1923). Über die Statistik Verketteter Vorgänge, Journal of Applied Mathematics and Mechanics 1: 279–289.Google Scholar
Englin, J. and Shonkwiler, J. (1995). Estimating social welfare using count data models: an application under conditions of endogenous stratification and truncation, Review of Economics and Statistics 77: 104–112.CrossRefGoogle Scholar
Fair, R. (1978). A theory of extramarital affairs, Journal of Political Economy 86: 45–61.CrossRefGoogle Scholar
Famoye, F. and Singh, K. (2006). Zero-truncated generalized Poisson regression model with an application to domestic violence, Journal of Data Science 4: 117–130.Google Scholar
Famoye, F (1995). Generalized binomial regression model, Biometrical Journal 37(5): 581–594.CrossRefGoogle Scholar
Faraway, J. (2006). Extending the Linear Model with R, Boca Raton, FL: Chapman & Hall/CRC Press.Google Scholar
Frees, E. (2004). Longitudinal and Panel Data, Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Goldberger, A. S. (1983). Abnormal selection bias, in Studies in Econometrics, Time Series, and Multivariate Statistics, ed. Karlin, S., Amemiya, T., and Goodman, L. A., New York: Academic Press, pp. 67–85.Google Scholar
Gould, W., Pitblado, J., and Sribney, W. (2006). Maximum Likelihood Estimation with Stata, Third Edition, College Station: Stata Press.Google Scholar
Greene, W. H. (1992). Statistical models for credit scoring, Working Paper, Department of Economics, Stern School of Business, New York University.Google Scholar
Greene, W. H. (1994). Accounting for excess zeros and sample selection in Poisson and negative binomial regression models, EC-94–10, Department of Economics, Stern School of Business, New York University.Google Scholar
Greene, W. H. (2003). Econometric Analysis, Fifth Edition, New York: Macmillan.Google Scholar
Greene, W. H. (2006). LIMDEP Econometric Modeling Guide, Version 9, Plainview, NY: Econometric Software Inc.Google Scholar
Greene, W. H. (2006a). A general approach to incorporating ‘selectivity’ in a model, Working Paper, Department of Economics, Stern School of Business, New York University.Google Scholar
Greenwood, M. and Yule, G. U. (1920) An inquiry into the nature of frequency distributions of multiple happenings, with particular reference to the occurrence of multiple attacks of disease or repeated accidents, Journal of the Royal Statistical Society A, 83: 255–279.CrossRefGoogle Scholar
Gurmu, S. and Trivedi, P. K. (1992). Overdispersion tests for truncated Poisson regression models, Journal of Econometrics 54: 347–370.CrossRefGoogle Scholar
Hardin, J. W. and Hilbe, J. M. (2001). Generalized Linear Models and Extensions, College Station, TX: Stata PressGoogle Scholar
Hardin, J. W. and Hilbe, J. M. (2002). Generalized Estimating Equations, Boca Raton, FL: Chapman & Hall/CRC Press.CrossRefGoogle Scholar
Hardin, J. W. and Hilbe, J. M. (2007). Generalized Linear Models and Extensions, Second Edition, College Station, TX: Stata PressGoogle Scholar
Hardin, J. W. (2003). The sandwich estimate of variance, in Advances in Econometrics: Maximum Likelihood of Misspecified Models: Twenty Years Later, ed. Fomby, T. and Hill, C., Elsevier, 17: 45–73.Google Scholar
Hausman, J., Hall, B., and Griliches, Z. (1984). Econometric models for count data with an application to the patents – R&D relationship, Econometrica 52: 909–938.CrossRefGoogle Scholar
Heckman, J. (1979). Sample selection bias as a specification error, Econometrica, 47: 153–161.CrossRefGoogle Scholar
Heilbron, D. (1989). Generalized linear models for altered zero probabilities and overdispersion in count data, Technical Report, Department of Epidemiology and Biostatistics, University of California, San Francisco.Google Scholar
Hilbe, J. M. (1993a). Log negative binomial regression as a generalized linear Model, technical Report COS 93/945–26, Department of Sociology, Arizona State University.Google Scholar
Hilbe, J. (1993b). Generalized linear models, Stata Technical Bulletin, STB-11, sg16.Google Scholar
Hilbe, J. (1993c). Generalized linear models using power links, Stata Technical Bulletin, STB-12, sg16.1Google Scholar
Hilbe, J. (1994a). Negative binomial regression, Stata Technical Bulletin, STB-18, sg16.5Google Scholar
Hilbe, J. (1994b). Generalized linear models, The American Statistician, 48(3): 255–265.Google Scholar
Hilbe, J. (2000). Two-parameter log-gamma and log-inverse Gaussian models, in Stata Technical Bulletin Reprints, College Station, TX: Stata Press, pp. 118–121.Google Scholar
Hilbe, J. (2005a). CPOISSON: Stata module to estimate censored Poisson regression, Boston College of Economics, Statistical Software Components, http://ideas.repec.org/c/boc/bocode/s456411.htmlGoogle Scholar
Hilbe, J. (2005b), CENSORNB: Stata module to estimate censored negative binomial regression as survival model, Boston College of Economics, Statistical Software Components, http://ideas.repec.org/c/boc/bocode/s456508.htmlGoogle Scholar
Hilbe, J. and Turlach, B. (1995). Generalized linear models, in XploRe: An Interactive Statistical Computing Environment, ed. Hardle, W., Klinke, S., and Tulach, B., New York: Springer-Verlag, pp. 195–222.Google Scholar
Hilbe, J. (1998). Right, left, and uncensored Poisson regression, Statistical Bulletin, 46: 18–20.Google Scholar
Hilbe, J. and Greene, W. (2007). Count response regression models, in Epidemiology and Medical Statistics, ed. Rao, C. R., Miller, J. P, and Rao, D. C., Elsevier Handbook of Statistics Series, London, UK: Elsevier.Google Scholar
Hoffmann, J. (2004). Generalized Linear Models, Boston, MA: Allyn & Bacon.Google Scholar
Hosmer, D. and Lemeshow, S. (2003). Applied Logistic Regression, second edition, New York: WileyGoogle Scholar
Huber, P. J. (1967). The behavior of maximum likelihood estimates under nonstandard conditions, in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 221–233.Google Scholar
Ihaka, R. and Gentleman, R. (1996). “R: A language for data analysis and graphics,” Journal of Computational and Graphical Statistics 5: 299–314.Google Scholar
Jain, G. C. and Consul, P. C. (1971). A generalized negative binomial distibution, SIAM Journal of Applied Mathematics 21(4): 501–513.CrossRefGoogle Scholar
Johnston, G. (1998). SAS/STAT/GENMOD procedure, SAS InstituteGoogle Scholar
Karim, M. R. and Zeger, S. (1989). A SAS macro for longitudinal data analysis, Department of Biostatistics, Technical Report 674, The John Hopkins University.Google Scholar
Katz, E. (2001). Bias in conditional and unconditional fixed effects logit estimation, Political Analysis 9(4): 379–384.CrossRefGoogle Scholar
King, G. (1988). Statistical models for political science event counts: bias in conventional procedures and evidence for the exponential Poisson regression model, American Journal of Political Science 32: 838–863.CrossRefGoogle Scholar
King, G. (1989). Event count models for international relations: generalizations and applications, International Studies Quarterly 33: 123–147.CrossRefGoogle Scholar
Lambert, D. (1992). Zero-inflated Poisson regression with an application to defects in manufacturing, Technometrics 34: 1–14.CrossRefGoogle Scholar
Lancaster, T. (2002). Orthogonal parameter and panel data, Review of Economic Studies 69: 647–666.CrossRefGoogle Scholar
Lawless, J. F. (1987). Negative binomial and mixed Poisson regression, Canadian Journal of Statistics 15, (3): 209–225.CrossRefGoogle Scholar
Lee, Y., Nelder, J., and Pawitan, Y. (2006). Generalized Linear Models with Random Effects, Boca Raton, FL: Chapman & Hall/CRC Press.CrossRefGoogle Scholar
Liang, K.-Y. and Zeger, S. (1986). Longitudinal data analysis using generalized linear models, Biometrika, 73: 13–22.CrossRefGoogle Scholar
Long, J. S. (1997). Regression Models for Categorical and Limited Dependent Variables, Thousand Oaks, CA: Sage.Google Scholar
Long, J. S. and Freese, J. (2003, 2006). Regression Models for Categorical Dependent Variables using Stata, Second Edition, College Station, TX: Stata Press.Google Scholar
Loomis, J. B. (2003). Travel cost demand model based river recreation benefit estimates with on-site and household surveys: comparative results and a correction procedure, Water Resources Research 39(4): 1105.CrossRefGoogle Scholar
Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters, Journal of the Society for Industrial and Applied Mathematics 11: 431–441.CrossRefGoogle Scholar
Martinez-Espiñeira, R., Amoako-Tuffour, J., and Hilbe, J. M. (2006), Travel cost demand model based river recreation benefit estimates with on site and household surveys: comparative results and a correction procedure – revaluation, Water Resource Research, 42.CrossRefGoogle Scholar
McCullagh, P. (1983). Quasi-likelihood functions, Annals of Statistics 11: 59–67.CrossRefGoogle Scholar
McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, Second Edition, New York: Chapman & Hall.CrossRefGoogle Scholar
Mullahy, J. (1986). Specification and testing of some modified count data models, Journal of Econometrics 33: 341–365.CrossRefGoogle Scholar
Mundlak, Y. (1978). On the pooling of time series and cross section data, Econometrica 46: 69–85.CrossRefGoogle Scholar
Mwalili, S., Lesaffre, E., and DeClerk, D. (2005). The zero-inflated negative binomial regresson model with correction for misclassification: an example in Caries Research, Technical Report 0462, LAP Statistics Network Interuniversity Attraction Pole, Catholic University of Louvain la Neuve, Belgium, www.stat.ucl.ac.be/IAP.Google Scholar
Nelder, J. A. (1994). Generalized linear models with negative binomial or beta-binomial errors, unpublished manuscript.Google Scholar
Nelder, J. A. and Wedderburn, R. W. M. (1972). Generalized linear models, Journal of the Royal Statistical Society, Series A, 135(3): 370–384.CrossRefGoogle Scholar
Nelder, J. and Pregibon, D. (1987). An extended quasi-likelihood function, Biometrika 74: 221–232.CrossRefGoogle Scholar
Nelder, J. A. and Lee, Y. (1992). Likelihood, quasi-likelihood, and pseudo-likelihood: some comparisons, Journal of the Royal Statistical Society, Series B, 54: 273–284.Google Scholar
Nelson, D. L. (1975). Some remarks on generalized of negative binomial and Poisson distributions, Technometrics 17: 135–136.CrossRefGoogle Scholar
Neyman, O, and Scott, E. L. (1948). Consistent estimation from partially consistent observations, Econometrica 16(1): 1–32.CrossRefGoogle Scholar
Pan, W. (2001). Akaike's information criterion in generalized estimating equations, Biometrics 57: 120–125.CrossRefGoogle ScholarPubMed
Pan, W. (2001a). On the robust variance estimator in generalized estimating equations, Biometrika 88(3): 901–906.CrossRefGoogle Scholar
Pierce, D. A. and Schafer, D. W. (1986). “Residuals in Generalized Linear Models,” Journal of the American Statistical Association, 81: 977–986.CrossRefGoogle Scholar
Plackett, R. L. (1981). The Analysis of Categorical Data (2nd ed.), London: Chapman and Hall.Google Scholar
Rabe-Hesketh, S. and Skrondal, A. (2004). Generalized Latent Variable Modeling, Boca Raton, FL: Chapman & Hall/CRC Press.Google Scholar
Rabe-Hesketh, S. and Skrondal, A. (2005). Multilevel and Longitudinal Modeling Using Stata, College Station, TX: Stata Press.Google Scholar
Sikkel, D. and Jelierse, G. (1998). Renewal theory and retrospective questions, Applied Statistics 37: 312–420.Google Scholar
Shaw, D. (1988). On-site samples' regression, Journal of Econometrics 37: 211–223.CrossRefGoogle Scholar
Terza, J. V. (1998). A Tobit-type estimator for the censored Poisson regression model, Econometric Letters 18: 361–365.CrossRefGoogle Scholar
Thall, P. and Vail, S. (1990). Some covariance models for longitudinal count data and overdispersion, Biometrika 46: 657–671.CrossRefGoogle ScholarPubMed
Twisk, J. W. R. (2003). Applied Longitudinal Data Analysis for Epidemiology, Cambridge: Cambridge University Press.Google Scholar
Vadeby, A. (2002). Estimation in a model with incidental parameters, LiTH-MAT-R-2002-02 working paper.Google Scholar
Venables, W. and Ripley, B. (2002). Modern Applied Statistics with S, Fourth Edition, New York: Springer-Verlag.CrossRefGoogle Scholar
Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses, Econometrica 57: 307–333.CrossRefGoogle Scholar
Wedderburn, R. W. M. (1974). Quasi-likelihood functions, generalized linear models and the Gauss–Newton method. Biometrika 61: 439–447.Google Scholar
, White H. (1980). A heteroskestasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity, Econometrica 48(4): 817–838.CrossRefGoogle Scholar
Winkelmann, R (1995). Duration dependence and dispersion in count-data models, Journal of Business and Economic Statistics 13: 467–474.Google Scholar
Winkelmann, R. (2003). Econometric Analysis of Count Data, Fourth Edition, Heidelberg, Germany: Springer-Verlag.CrossRefGoogle Scholar
Winkelmann, R., and Zimmermann, K. F. (1995). Recent developments in count data modelling: theory and application, Journal of Economic Surveys, 9: 1–24.CrossRefGoogle Scholar
Woutersen, T. (2002). Robustness against incidental parameters, Department of Economics Working paper 2008, University of Western Ontario.Google Scholar
Yang, Z., Hardin, J., and Abby, C. (2006). A score test for overdispersion based on generalized Poisson model, unpublished manuscript.Google Scholar
Zeger, S. L., Liang, K.-Y., and Albert, P. S. (1988). Models for longitudinal data: A generalized estimating equation approach, Biometrics 44: 1049–1060.CrossRefGoogle ScholarPubMed

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  • References
  • Joseph M. Hilbe, Arizona State University
  • Book: Negative Binomial Regression
  • Online publication: 05 June 2012
  • Chapter DOI: https://doi.org/10.1017/CBO9780511811852.018
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  • References
  • Joseph M. Hilbe, Arizona State University
  • Book: Negative Binomial Regression
  • Online publication: 05 June 2012
  • Chapter DOI: https://doi.org/10.1017/CBO9780511811852.018
Available formats
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  • References
  • Joseph M. Hilbe, Arizona State University
  • Book: Negative Binomial Regression
  • Online publication: 05 June 2012
  • Chapter DOI: https://doi.org/10.1017/CBO9780511811852.018
Available formats
×