Skip to main content Accessibility help
×
  • Cited by 1518
Publisher:
Cambridge University Press
Online publication date:
June 2012
Print publication year:
2011
Online ISBN:
9780511973420

Book description

This second edition of Hilbe's Negative Binomial Regression is a substantial enhancement to the popular first edition. The only text devoted entirely to the negative binomial model and its many variations, nearly every model discussed in the literature is addressed. The theoretical and distributional background of each model is discussed, together with examples of their construction, application, interpretation and evaluation. Complete Stata and R codes are provided throughout the text, with additional code (plus SAS), derivations and data provided on the book's website. Written for the practising researcher, the text begins with an examination of risk and rate ratios, and of the estimating algorithms used to model count data. The book then gives an in-depth analysis of Poisson regression and an evaluation of the meaning and nature of overdispersion, followed by a comprehensive analysis of the negative binomial distribution and of its parameterizations into various models for evaluating count data.

Reviews

‘Students, developers, and practitioners in this area will all want to have this thorough guide close at hand. The wealth of theory and extensive applications using ‘real' data sets and contemporary software will provide a crucial resource for their research.'

William Greene - New York University

‘This is a well-researched practically oriented book on an important class of models relevant to over-dispersed count data. Recommended.'

John Nelder - Imperial College London

‘Every model currently offered in commercial statistical software is discussed in detail … well written and can serve as an excellent reference book for applied statisticians who would use negative binomial regression modelling for undergraduate students or graduate students.'

Yuehua Wu Source: Zentralblatt MATH

‘I would recommend this book to researchers and students who would like to gain an overview of the negative binomial distribution and its extensions.'

Fiona McElduff - University College London

‘The text is well-written, easy-to-read but once started, is difficult to put down as each chapter unfolds the intricacies of the distribution.'

Source: International Statistical Review

'The second edition of Negative Binomial Regression is a unique statistical textbook. It is a very enjoyable read! It not only provides statistical fundamentals, but also provides historical perspectives and expert insights. This book is an excellent introduction for someone new to modeling count data, as well as an invaluable resource for the experienced practitioner grappling with complex overdispersed data.'

Elizabeth Kelly - Statistical Sciences Group, Los Alamos National Laboratory

'As with all of Joe Hilbe's books this text is thorough and scholarly with an extensive list of references. Important theorems and other theoretical results are presented but are presented to be informative rather than to develop and teach the theory.'

Michael R. Chernick Source: Significance

'… a valuable hands-on introduction to negative binomial regression and the analysis of count data in general. I am also pleased to see an advocation of the utility of the negative binomial distribution in applied work.'

Source: Psychometrika

Refine List

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
×

Contents

References and further reading
Akaike, H. (1973). Information theory and extension of the maximum likelihood principle, in Second international Symposium on Information Theory, ed. Petrov, B. N. and Csaki, F., Budapest: Akademiai Kiado, pp. 267–281.
Allison, P. D. and Waterman, R (2002). Fixed-effects negative binomial regression models, unpublished manuscript.
Amemiya, T. (1984). Tobit models: A survey, Journal of Econometrics 24: 3–61.
Anscombe, F. J. (1948). The transformations of Poisson, binomial, and negative binomial data, Biometrika 35: 246–254.
Anscombe, F. J. (1949). The statistical analysis of insect counts based on the negative binomial distribution, Biometrics 5: 165–173.
Anscombe, F. J. (1950), Sampling theory for the Negative Binomial and Logarithmic Series Distributions, Biometrika 37 (3/4): 368–382.
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.
Bartlett, M. S. (1947). The use of transformations, Biometrics 3: 39–52.
Beall, G. (1942). The Transformation of data from entomological field experiments so that that analysis of variance becomes applicable, Biometrika 29: 243–262.
Bliss, C. I. (1958). The analysis of insect counts as negative binomial distributions. Proceedings of the Tenth International Congress on. Entomology 2: 1015–1032.
Bliss, C. I., and Fisher, R. A. (1953). Fitting the negative binomial distribution to biological data and note on the efficient fitting of the negative binomial, Biometrics 9: 176–200.
Bliss, C. I. and Owen, A. R. G. (1958). Negative binomial distributions with a common k, Biometricka 45: 37–58.
Blom, G. (1954). Transformations of the binomial, negative binomial, Poisson, and χ2 distributions, Biometrika 41: 302–316.
Boswell, M. T. and Patil, G. P. (1970). Chance mechanisms generating the negative binomial distribution, in Random Counts in Models and Structures, Volume 1, ed. G. P. Patil, University Park, PA: Pennsylvania State University Press, pp. 1–22.
Bozdogan, H. (1987). Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions, Special Section, Psychometrika 52(3): 345–370.
Breslow, N. E. (1984). Extra-Poisson variation in log-linear models, Applied Statistics 33 (1): 38–44.
Bulmer, M. G. (1974). On Fitting The Poisson Lognormal Distribution to Species-Abundance Data, Biometrics 30:101–110.
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.
Cameron, A. C. and Trivedi, P. K. (1990). Regression-based tests for overdispersion in the Poisson model, Journal of Econometrics 46: 347–364.
Cameron, A. C. and Trivedi, P. K. (1998). Regression Analysis of Count Data, New York: Cambridge University Press.
Cameron, A. C. and Trivedi, P. K. (2010). Microeconometrics Using Stata, revised edition, College Station, TX: Stata Press.
Collett, D. (1989). Modelling Binary Data, London: Chapman & Hall.
Consul, P. and Jain, G (1973). A generalization of the Poisson distribution, Technometrics 15: 791–799.
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.
Consul, P. and Famoye, F. (1992). Generalized Poisson regression model, Communications in statistics – Theory and Method 21: 89–109.
Cui, J. (2007). QIC program and model selection in GEE analysis, Stata Journal 7(2): 209–220.
Dean, C. and Lawless, J. F. (1989). Tests for detecting overdispersion in Poisson regression models, Journal of the American Statistical Association 84: 467–472.
Deb, P. and Trivedi, P. K. (2002), The structure of demand for medical care: latent class versus two-part models, Journal of Health Economics 21: 601–625.
Deb, P. and Trivedi, P. K. (2006), Maximum simulated likelihood estimation of a negative binomial regression model with multinomial endogeneous treatment, Stata Journal 6: 246–255.
Dohoo, I., Martin, W. and Stryhn, H. (2010). Veterinary Epidemiologic Research, Charlottetown, Prince Edward island, CA: VER, Inc.
Drescher, D. (2005). Alternative distributions for observation driven count series models, Economics Working Paper No 2005–11, Christian-Albrechts-Universitat, Kiel, Germany.
Dunn, P.K. and Smyth, G.K. (1996). Randomized quantile residuals, Journal of Computational and Graphical Statistics 5(3): 236–244.
Edwards, A. W. F. (1972). Likelihood, Baltimore, MD: Johns Hopkins University Press.
Eggenberger, F. and Polya, G. (1923). Uber die Statistik Verketteter Vorgange, Journal of Applied Mathematics and Mechanics 1: 279–289.
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.
Evans, D. A. (1953). Experimental evidence concerning contagious distributions in ecology, Biometrika 40: 186–211.
Fair, R. (1978). A theory of extramarital affairs, Journal of Political Economy 86: 45–61.
Famoye, F. (1995). Generalized binomial regression model, Biometrical Journal 37(5): 581–594.
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.
Faraway, J. (2006). Extending the Linear Model with R, Boca Raton, FL: Chapman & Hall/CRC Press.
Fisher, R. A. (1941). The negative binomial distribution, Annals of Eugenics, 11, 182–187.
Frees, E. (2004). Longitudinal and Panel Data, Cambridge: Cambridge University Press.
Fridstrøm, L., Ifver, J., Ingebrigsten, S., Kulmala, R. and Thomsen, L. K. (1995). Measuring the contribution of randomness, exposure, weather, and daylight to the variation in the road accident counts, Accident Analysis and Prevention 27(1): 1–20.
Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (2004). Bayesian Data Analysis, second edition, Boca Raton, FL: Chapman & Hall/CRC.
Gelman, A. and Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge: Cambridge University Press.
Gerdtham, U. G. (1997). Equity in health care utilization: Further tests based on hurdle models and Swedish micro data, Health Economics 6: 303–319.
Geweke, J.F. (2005). Contemporary Bayesian Econometrics and Statistics, New York: Wiley.
Gill, J. (2002). Bayesian Methods, Boca Raton, FL: Chapman & Hall/CRC.
Gill, J. (2010). Critical differences in Bayesian and non-Bayesian inference and why the former is better, in Statistics in the Social Sciences, ed. S. Kolenikov, D. Steinley and L. Thombs, New York: Wiley.
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.
Gould, W., Pitblado, J. and Scribney, W. (2006). Maximum Likelihood Estimation with Stata, third edition, College Station, TX: Stata Press.
Gourieroux, C., Monfort, A. and Trognon, A. (1984). Pseudo maximum likelihood methods: theory, Econometrica 52: 681–700.
Greene, W. H. (1992). Statistical Models for Credit Scoring, Working Paper, Department of Economics, Stern School of Business, New York University.
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.
Greene, W. H. (2006a). LIMDEP Econometric Modeling Guide, Version 9, Plainview, NY: Econometric Software Inc.
Greene, W. H. (2006b). A general approach to incorporating ‘selectivity’ in a model, Working Paper, Department of Economics, Stern School of Business, New York University.
Greene, W. H. (2007). Econometric Analysis, fifth edition, New York: Macmillan.
Greenwood, M. and Woods, H. M. (1919). On the Incidence of the Industrial Accidents upon Individuals with special Reference to Multiple Accidents. Report of the Industrial Fatigue Research Board. 4, 1–28. London: His Majesty's Stationery Office.
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.
Gurmu, S. and Trivedi, P. K. (1992). Overdispersion tests for truncated Poisson regression models, Journal of Econometrics 54: 347–370.
Hannan, E. J. and Quinn, B. G. (1979) The determination of the order of an autoregression, Journal of the Royal Statistical Society, B 41: 190–195.
Hardin, J. W. (2003). The sandwich estimate of variance, in Maximum Likelihood of Misspecified Models: Twenty Years Later, ed. Fomby, T. and Hill, C., Advances in Econometrics volume 17, Oxford: Elsevier, pp. 45–73.
Hardin, J. W. and Hilbe, J. M. (2001). Generalized Linear Models & Extensions, College Station, TX: Stata Press.
Hardin, J. W. and Hilbe, J. M. (2002). Generalized Estimating Equations, Boca Raton, FL: Chapman & Hall/CRC Press.
Hardin, J. W. and Hilbe, J. M. (2007). Generalized Linear Models & Extensions, second edition, College Station, TX: Stata Press.
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.
Heckman, J. (1979). Sample selection bias as a specification error, Econometrica 47: 153–161.
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.
Hilbe, J. M. (1993a). Log negative binomial regression as a generalized linear model, Technical Report COS 93/94–5-26, Department of Sociology, Arizona State University.
Hilbe, J. M. (1993b). Generalized linear models, Stata Technical Bulletin, STB-11, sg16.
Hilbe, J. M. (1993c). Generalized linear models using power links, Stata Technical Bulletin, STB-12, sg16.1.
Hilbe, J. M. (1994a). Negative binomial regression, Stata Technical Bulletin, STB-18, sg16.5.
Hilbe, J. M. (1994b). Generalized linear models, The American Statistician, 48(3): 255–265.
Hilbe, J. M. (2000). Two-parameter log-gamma and log-invese Gaussian models, in Stata Technical Bulletin Reprints, College Station,TX: Stata Press, pp. 118–121.
Hilbe, J. M. (2005a). CPOISSON: Stata module to estimate censored Poisson regression, Boston College of Economics, Statistical Software Components, http://ideas.repec.org/c/boc/bocode/s456411.html
Hilbe, J. M. (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.html
Hilbe, J. M. (2005c), Censored negative binomial regression, EconPapers, RePec, Research Papers in Economics, Boston School of Economics, Nov 30, 2005. http://fmwww.bc.edu/repec/bocode/c/censornb.ado
Hilbe, J. M. (2009). Logistic Regression Models, Boca Raton, FL: Chapman & Hall/CRC.
Hilbe, J. M. (2010a), Modeling count data, in International Encyclopedia of Statistical Science, ed. M. Lovric, New York: Springer.
Hilbe, J. M. (2010b), Generalized linear models, in International Encyclopedia of Statistical Science, ed. M. Lovric, New York: Springer.
Hilbe, J. M. (2010c), Creating synthetic discrete-response regression models, Stata Journal 10(1): 104–124.
Hilbe, J. M. (2011). Negative Binomial Regression Extensions. Cambridge University Press website for the text: www.cambridge.org/9780521857727.
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.
Hilbe, J. and Judson, D. (1998). Right, left, and uncensored Poisson regression, in Stata Technical Bulletin Reprints, College Station, TX: Stata Press, pp. 186–189.
Hilbe, J. and Greene, W. (2007). Count response regression models, inEpidemiology and Medical Statistics, ed. Rao, C. R., Miller, J. P. and Rao, D. C., Elsevier Handbook of Statistics Series, London: Elsevier.
Hilbe, J.M and Robinson, A.P. (2011), Methods of Statistical Model Estimation, Boca Raton, FL: Chapman & Hall/CRC.
Hin, L.-Y. and Wang, Y.-G. (2008). Working-correlation-structure identification in generalized estimating equations, Statistics in Medicine 28(4): 642–658.
Hinde, J. and Demétrio, C. G. B (1998). Overdispersion: models and estimation, Computational Statistics & Data Analysis 27(2): 151–170
Hoffman, J. (2004). Generalized Linear Models, Boston, MA: Allyn and Bacon.
Hosmer, D. and Lemeshow, S. (2000), Applied Logistic Regression, second edition, New York: Wiley.
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, Berkeley, CA: University of California Press, pp. 221–233.
Irwin, J. O. (1968). The generalized Waring distribution applied to accident theory, Journal of the Royal Statistical Society, A 131(2): 205–225.
Iwasaki, M. and Tsubaki, H (2005a). A bivariate generalized linear model with an application to meteorological data analysis, Statistical Methodology 2: 175–190.
Iwasaki, M. and Tsubaki, H. (2005b). A new bivariate distribution in natural exponential family, Metrika 61: 323–336
Iwasaki, M. and Tsubaki, H. (2006). Bivariate negative binomial generalized linear models for environmental count data, Journal of Applied Statistics 33(9): 909–923.
Jain, S. K. (1959). Fitting the negative binomial distribution to some data on asynaptic behaviour of chromosomes, Genetica 30: 108–122.
Jain, G. C. and Consul, P. C. (1971). A generalized negative binomial distibution, SIAM Journal of Applied Mathematics, 21(4): 501–513.
Johnson, N. L., Kemp, A. W. and Kotz, S. (2005). Univariate Discrete Distributions, third edition, New York: Wiley.
Jones, A. M., Rice, N., d'Uva, T.B. and Balia, S. (2007). Applied Health Economics, New York & Oxford: Routledge, Taylor and Francis.
Jones, O., Maillardet, R. and Robinson, A. (2009). Scientific Programming and Simulation Using R, Boca Raton, FL: Chapman & Hall/CRC
Karim, M. R. and Zeger, S. (1989). A SAS macro for longitudinal data analysis, Department of Biostatistics, the Johns Hopkins University: Technical Report 674.
Katz, E. (2001). Bias in conditional and unconditional fixed effects logit estimation, Political Analysis 9(4): 379–384.
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.
King, G. (1989). Event count models for international relations: generalizations and applications, International Studies Quarterly 33: 123–147.
Lambert, D. (1992). Zero-inflated Poisson regression with an application to defects in manufacturing, Technometrics, 34: 1–14.
Lancaster, T. (2002). Orthogonal parameter and panel data, Review of Economic Studies 69: 647–666.
Lawless, J. F. (1987). Negative binomial and mixed Poisson regression, Canadian Journal of Statistics 15(3): 209–225.
Lee, Y., Nelder, J. and Pawitan, Y. (2006). Generalized Linear Models with Random Effects, Boca Raton, FL: Chapman & Hall/CRC Press.
Leisch, F. and Gruen, B. (2010). Flexmix: Flexible Mixture Modeling, CRAN
Liang, K.-Y. and Zeger, S. (1986). Longitudinal data analysis using generalized linear models, Biometrika 73: 13–22.
Long, J. S. (1997). Regression Models for Categorical and Limited Dependent Variables, Thousand Oaks, CA: Sage.
Long, J. S. and Freese, J. (2006). Regression Models for Categorical Dependent Variables using Stata, second edition, College Station, TX: Stata Press.
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.
Machado, J. A. F. and Santos Silva, J. M. C. (2005), Quantiles for counts, Journal of the American Statistical Association 100: 1226–1237.
Marquardt, D. W. (1963). An algorithm for least-squares estimation of non-linear parameters, Journal of the Society for Industrial and Applied Mathematics 11: 431–441.
Martinez-Espiñeira, R., Amaoko-Tuffor, 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.
McCullagh, P. (1983). Quasi-likelihood functions, Annals of Statistics 11: 59–67.
McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, second edition, New York: Chapman & Hall.
Melkersson, M. and Roth, D. (2000). Modeling of household fertility using inflated count data models, Journal of Population Economics 13: 189–204.
Miaou, S.-P. (1996). Measuring the goodness-of-fit of accident prediction models, FHWA-RD-96–040, Federal Highway Administration, Washington, DC.
Muenchen, R. A. and Hilbe, J. M. (2010). R for Stata Users, New York: Springer.
Mullahy, J. (1986). Specification and testing of some modified count data models, Journal of Econometrics 33: 341–365.
Mundlak, Y. (1978). On the pooling of time series and cross section data, Econometrica 46: 69–85.
Murphy, K. and Topel, R. (1985). Estimation and inference in two step econometric models, Journal of Business and Economic Statistics 3:370–379.
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.
Nelder, J. A. (1994). Generalized linear models with negative binomial or beta-binomial errors, unpublished manuscript.
Nelder, J. A. and Wedderburn, R. W. M. (1972). Generalized linear models, Journal of the Royal Statistical Society, A 135(3): 370–384.
Nelder, J. A. and Pregibon, D. (1987). An extended quasi-likelihood function, Biometrika 74: 221–232.
Nelder, J. A. and Lee, Y. (1992). Likelihood, quasi-likelihood, and pseudo-likelihood: some comparisons, Journal of the Royal Statistical Society, B 54: 273–284.
Nelson, D. L. (1975). Some remarks on generalized of negative binomial and Poisson distributions, Technometrics 17: 135–136.
Newbold, E. M. (1927). Practical applications of the statistics of repeated events, particularly to industrial accidents. Journal of the Royal Statistical Society 90: 487–547.
Neyman, J. and Scott, E. L. (1948). Consistent estimation from partially consistent observations, Econometrica, 16(1): 1–32
Nylund, K. L, Asparouhov, T., and Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study, Structural Equation Modeling 14(4): 535–569.
Pan, W. (2001a). Akaike's information criterion in generalized estimating equations, Biometrics 57: 120–125.
Pan, W. (2001b). On the robust variance estimator in generalized estimating equations, Biometrika 88(3): 901–906.
Piegorsch, W. W. (1990) Maximum likelihood estimation for the negative binomial dispersion parameter, Biometrics 46: 863–867.
Pierce, D. A. and Schafer, D. W. (1986). Residuals in generalized linear models, Journal of the American Statistical Association 81: 977–986.
Rabe-Hesketh, S. and Skrondal, A. (2004). Generalized Latent Variable Modeling, Boca Raton, FL: Chapman & Hall/CRC Press.
Rabe-Hesketh, S. and Skrondal, A. (2005). Multilevel and Longitudinal Modeling Using Stata, College Station, TX: Stata Press.
Rigby, R. and Stasinopoulos, M (2008). A fexible regression approach using GAMLSS in R, Handout for a short course in GAMLSS given at International Workshop of Statistical Modelling, University of Utrecht.
Rodríguez-Avi, J., Conde-Sánchez, A., Sáez-Castillo, A. J., Olmo-Jiménez, M. J., and Martínez-Rodríguez, A. M. (2009), A generalized Waring regression model for count data, Computational Statistics & Data Analysis 53(10): 3717–3725.
Rouse, D. M. (2005). Estimation of finite mixture models, Masters thesis, North Carolina State University.
,SAS/STAT 9.22User's Guide (2010), Cary, NC: SAS Institute
Shaw, D. (1988). On-site samples' regression, Journal of Econometrics 37: 211–223.
Shults, J., Sun, W., Tu, X., Kim, H., Amsterdam, J., Hilbe, J. M. and Ten-Have, T. (2009). Comparison of several approaches for choosing between working correlation structures in generalized estimating equation analysis of longitudinal binary data, Statistics in Medicine 28: 2338–2355.
Simon, L. J. (1960). The negative binomial and Poisson distributions compared, Proceedings of the Casuality and Actuarial Society 47: 20–24.
Simon, L. (1961). Fitting negative binomial distributions by the method of maximum likelihood, Proceedings of the Casuality and Actuarial Society 48: 45–53.
Stata Reference Manual, version 11 (2009), College Station, TX: Stata Press.
Student (1907). On the error of counting with a haemacytometer, Biometrika 5: 351–360.
Terza, J. V. (1998). A tobit-type estimator for the censored Poisson regression model, Econometric Letters 18: 361–365.
Thall, P. and Vail, S (1990). Some covariance models for longitudinal count data and overdispersion, Biometrika 46: 657–671.
Twist, J. W. R. (2003). Applied Longitudinal Data Analysis for Epidemiology, Cambridge: Cambridge University Press.
Vadeby, A. (2002). Estimation in a model with incidental parameters, LiTH-MAT-R-2002–02 working paper.
Venables, W. and Ripley, B. (2002). Modern Applied Statistics with S, fourth edition, New York: Springer-Verlag.
Vogt, A. and Bared, J. G. (1998). Accident models for two-lane rural roads: segments and intersections, publication No. FHWA-RD-98–133, Federal Highway Administration, Washington, DC, http://www.tfhrc.gov/safety/98133/ch05/ch05_01.html
Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses, Econometrica 57: 307–333.
Wedderburn, R. W. M. (1974). Quasi-likelihood functions, generalized linear models and the Gauss–Newton method, Biometrika 61: 439–47.
White, H. (1980). A heteroskestasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity, Econometrica 48(4): 817–838.
Williamson, E. and Bretherton, M. H. (1963). Tables of the Negative Binomial Distribution, New York:Wiley.
Winkelmann, R. (1995). Duration dependence and dispersion in count-data models, Journal of Business and Economic Statistics 13: 467–474.
Winkelmann, R. (2008). Econometric Analysis of Count Data, Fifth Edition, New York: Springer.
Winklemann, R. and Zimmermann, K. F. (1995). Recent developments in count data modelling, theory and application, Journal of Economic Surveys 9: 1–24.
Woutersen, T. (2002). Robustness against incidental parameters, University of Western Ontario, Department of Economics Working papers, 20028.
Xekalaki, E. (1983). The univariate generalized Waring distribution in relation to accident theory: Proneness, spells or contagion?, Biometrics 39(3): 39–47.
Xekalaki, E. (1984). The bivariate generalized Waring distribution and its application to accident theory, Journal of the Royal Statistical Society A 147(3): 488–498.
Yang, Z., Hardin, J. and Abby, C. (2006). A score test for overdispersion based on generalized poisson model, Unpublished manuscript.
Yule, G. U. (1910). On the distribution of deaths with age when the causes of death act cumulatively, and similar frequency distributions, Journal of the Royal Statistical Society 73: 26–35.
Zeger, S. L., Liang, K. -Y., and Albert, P. S. (1988). Models for longitudinal data: A generalized estimating equation approach, Biometrics 44: 1049–1060.

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Book summary page views

Total views: 0 *
Loading metrics...

* Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

Usage data cannot currently be displayed.