Skip to main content Accessibility help
×
Hostname: page-component-7bb8b95d7b-s9k8s Total loading time: 0 Render date: 2024-09-07T07:19:18.550Z Has data issue: false hasContentIssue false

Bibliography

Published online by Cambridge University Press:  05 August 2014

Joseph M. Hilbe
Affiliation:
Arizona State University
Get access
Type
Chapter
Information
Modeling Count Data , pp. 269 - 276
Publisher: Cambridge University Press
Print publication year: 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Akaike, H. 1973. “Information Theory and Extension of the Maximum Likelihood Principle,” in Second International Symposium on Information Theory, ed. B. N., Petrov and F., Csaki, pp. 267-281. Budapest: Akademiai Kiado.Google Scholar
Amemiya, T. 1984. “Tobit Models: A Survey.” Journal of Econometrics 24: 3–61.CrossRefGoogle Scholar
Anscombe, F. J. 1953. “Contribution to the Discussion of H. Hotelling's Paper.” Journal of the Royal Statistical SocietySeries B 15 (no. 1): 229–230.Google Scholar
Bailey, M., M. A., Collins, J. D. M., Gordon, A. F., Zuur, and I. G., Priede. 2008. “Long-term Changes in Deep-water Fish Populations in the North East Atlantic: A Deeper-Reaching Effect of Fisheries?Proceedings of the Royal Society B 275: 1965–1969.Google Scholar
Barnett, A., N., Koper, A., Dobson, F., Schmiegelow, and M., Manseau. 2010. “Using Information Criteria to Select the Correct VarianceENCovariance Structure for Longitudinal Data in Ecology.” Methods in Ecology and Evolution 1 (no. 1): 15–24.CrossRefGoogle Scholar
Cameron, A. C., and P. K., Trivedi (1998). Regression Analysis of Count Data. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Carroll, R. J., and D., Ruppert. 1981. “On Prediction and the Power Transformation Family.” Biometrika 68: 609–615.CrossRefGoogle Scholar
Consul, P. C. 1989. Generalized Poisson Distributions: Properties and Applications. New York: Marcel Dekker.Google Scholar
Conway, R. W., and W. L., Maxwell. 1962. “A Queuing Model with State Dependent Service Rates.” Journal of Industrial Engineering 12: 132–136.Google Scholar
Cragg, J. C. 1971. “Some Statistical Models for Limited Dependent Variables with Application to the Demand for Durable Goods.” Econometrica 39: 829–844.CrossRefGoogle Scholar
Dean, C., and J. F., Lawless. 1989. “Tests for Detecting Overdispersion in Poisson Regression Models.” Journal ofthe American Statistical Association 84: 467–472.Google Scholar
Desmarais, B. A., andJ. J. Harden. 2013. “Testing for Zero Inflation in Count Models: Bias Correction for the Vuong Test.” Stata Journal 13 (no. 4): 810–835.Google Scholar
Dohoo, I., W., Martin, and H., Stryhn. 2012. Methods in Epidemiologic Research.Charlottetown: VER Publishing.Google Scholar
Efron, B. 1986. “Double Exponential Families and Their Use in Generalized Linear Regression.” Journal of the American Statistical Association 81: 709–721.CrossRefGoogle Scholar
Ellis, P. D. (2010). The Essential Guide to Effect Sizes. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Faddy, M., and D., Smith. 2012. “Analysis of Count Data with Covariate Dependence in Both Mean and Variance.” Journal ofApplied Statistics 38: 2683–2694.Google Scholar
Famoye, F. 1993. “Restricted Generalized Poisson Regression Model.” Communications in Statistics, Theory and Methods 22: 1335–1354.CrossRefGoogle Scholar
Famoye, F. and K., Singh. 2006. “Zero-Truncated Generalized Poisson Regression Model with an Application to Domestic Violence.” Journal of Data Science 4: 117–130.Google Scholar
Fabermacher, H. 2011. “Estimation of Hurdle Models for Overdispersed Count Data.” Stata Journal 11 (no. 1): 82-94.Google Scholar
Fabermacher, H. 2013. “Extensions of Hurdle Models for Overdispersed Count Data.” Health Economics 22 (no.11): 1398–1404.Google Scholar
Faraway, J. J. 2006. Extending the Linear Model with R.Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Flaherty, S., G., Patenaude, A., Close, and P. W. W., Lutz. 2012. “The Impact of Forest Stand Structure on Red Squirrel Habitat Use.” Forestry 85: 437–444.CrossRefGoogle Scholar
Geedipally, S. R., D., Lord, and S. S., Dhavala. 2013. “A Caution about Using Deviance Information Criterion While Modeling Traffic Crashes.” Unpublished manuscript.Google Scholar
Gelman, A., and J., Hill. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models.Cambridge: Cambridge University Press.Google Scholar
Goldberger, A. S. 1983. “Abnormal Selection Bias,” in Studies in Econometrics, Time Series, and Multivariate Statistics,” ed. S., Karlin, T., Amemiya, and L. A., Goodman, pp. 67-85. New York: Academic Press.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. 2008. Functional Forms for the Negative Binomial Model for Count Data, Economics Letters, 99 (no. 3): 585–590.CrossRefGoogle Scholar
Hamilton, L. C. 2013. Statistics with Stata, Version 12.Boston: Brooks-Cole.Google Scholar
Hannan, E. J., and B. G., Quinn. 1979. “The Determination of the Order of an Autoregression.” Journal ofthe Royal Statistical Society Series B 41: 190–195.Google Scholar
Hardin, J. W. 2003. “The Sandwich Estimate of Variance,” in Maximum Likelihood of Mis-specified Models: Twenty Years Later, ed. T., Fomby and C., Hill, pp. 45-73. Elsevier: Amsterdam.Google Scholar
Hardin, J. W. and J. M., Hilbe. 2013a. Generalized Linear Models and Extensions, third edition. College Station, TX: Stata Press/CRC.Google Scholar
Hardin, J. W. and J. M., Hilbe. 2013b. Generalized Estimating Equations, second edition. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Hardin, J. W. and J. M., Hilbe. 2014a. “Regression Models for Count Data Based on the Negative Binomial(p) Distribution.” Stata Journal 14.Google Scholar
Hardin, J. W. and J. M., Hilbe.2014b. “Truncated Regression Models for Count Data.” Stata Journal 14.
Harris, T., J. M., Hilbe, and J. W., Hardin.2013. “Modeling Count Data with Generalized Distributions.” Stata Journal.
Harris, T., Z., Yang, and J. W., Hardin. 2012. “Modeling Underdispersed Count Data with Generalized Poisson Regression.” Stata Journal 12 (no. 4): 736–747.Google Scholar
Hastie, T., and R., Tibshirani. 1986. “Generalized Additive Models.” Statistical Science 1 (no. 3): 297-318.Google Scholar
Hastie, T., and R., Tibshirani. 1990. Generalized Additive Models.New York: Chapman & Hall.Google Scholar
Hausman, J. A. 1978. “Specification Tests in Econometrics.” Econometrica 46: 1251–1271.CrossRefGoogle Scholar
B., Hall, and Z., Griliches. 1984. “Econometric Models for Count Data with an Application to the Patents-R&D Relationship.” Econometrica 52: 909–938.Google 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. “Generalized Linear Models.” Stata Technical Bulletin 11: sg16.Google Scholar
Hilbe, J. M. 1993b. “Generalized Linear Models Using Power Links.” Stata Technical Bulletin 12: sg16.1.Google Scholar
Hilbe, J. M. 1993c. “Log Negative Binomial Regression as a Generalized Linear Model.” Technical Report COS 93/94-5-26, Department of Sociology, Arizona State University. 1994a. “Negative Binomial Reegression.” Stata Technical Bulletin 18:sg16.5.Google Scholar
Hilbe, J. M. 1994b. “Generalized Linear Models.” The American Statistician 48 (no. 3): 255–265.Google Scholar
Hilbe, J. M. 1998. “Right, Left, and Uncensored Poisson Regression.” Stata Technical Bulletin 46: 18–20.Google Scholar
Hilbe, J. M. 2000. “Two-Parameter log-gamma and log-inverse Gaussian Models,” in Stata Technical Bulletin Reprints, pp.118-121. College Station, TX: Stata Press.Google Scholar
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.Google Scholar
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.Google Scholar
Hilbe, J. M. 2007a. Negative Binomial Regression. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Hilbe, J. M. 2007b. “The Co-evolution of Statistics and Hz,” in Real Data Analysis, ed. S. S., Sawilowsky, pp. 3-20. Charlotte, NC: Information Age Publishing.Google Scholar
Hilbe, J. M. 2009a. Logistic Regression Models. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Hilbe, J. M. 2009b. “CPOISSONE: Stata Module to Estimate Censored Poisson Regression (Econometric Parameterization).” Boston College of Economics, Statistical Software Components, http://ideas.repec.org/c/boc/bocode/s457079.html.Google Scholar
Hilbe, J. M. 2009c. Solutions Manual for Logistic Regression Models. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Hilbe, J. M. 2010a. “Modeling Count Data,” in International Encyclopedia of Statistical Science, ed. M., Lovric. New York: Springer.Google Scholar
Hilbe, J. M. 2010b. “Generalized Linear Models,” in International Encyclopedia of Statistical Science, ed. M., Lovric. New York: Springer.Google Scholar
Hilbe, J. M. 2011. Negative Binomial Regression, second edition. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Hilbe, J. M. 2012. Astrostatistical Challenges for the New Astronomy. New York: Springer.Google Scholar
Hilbe, J. M. and A. P., Robinson. 2013. Methods ofStatistical Model Estimation. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Hilbe, J. M., and W. H., Greene. 2008. “Count Response Regression Models,” in Handbook of Statistics, vol. 27, ed. C. R., Rao, J. P., Miller, and D. C., Rao, pp. 210–252. Amsterdam: Elsevier.Google Scholar
Hin, L., and Y., Wang. 2008. “Working-Correlation-Structure Identification in Generalized Estimating Equations.” Statistics in Medicine 28: 642–658.Google Scholar
Hinde, J., and C. G. B., Demietrio. 1998. “Overdispersion: Models and Estimation.” Computational Statistics and Data Analysis 27 (no. 2): 151–170.CrossRefGoogle Scholar
Huber, P. J. 1964. “Robust Estimation of Location Parameter.” The Annals of Mathematical Statistics 35 (no. 1).CrossRefGoogle 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. Berkeley: University of California Press.Google Scholar
Hurvich, C. M., and C., Tsai. 1989. “Regression and Time Series Model Selection in Small Samples.” Biometrika 76 (no. 2): 297-307.CrossRefGoogle Scholar
Irwin, J. O. 1968. “The Generalized Waring Distribution Applied to Accident Theory.” Journal ofthe Royal Statistical Society Series A 131 (no. 2): 205–225.Google Scholar
Lawless, J. F. 1987. “Negative Binomial and Mixed Poisson Regression.” Canadian Journal ofStatistics 15 (no. 3): 209-225.Google Scholar
Leisch, F., and B., Gruen. 2010. Flexmix: Flexible Mixture Modeling.CRAN.Google Scholar
Long, J. S. 1997. Regression Models for Categorical and Limited Dependent Variables.Thousand Oaks, CA: Sage Publications.Google Scholar
Long, J. S. and J., Freese. 2006. Regression Models for Categorical Dependent Variables Using Stata, second edition. College Station, TX: Stata Press.Google Scholar
Lord, D., S. E., Guikema, and S. R., Geedipally. 2007. “Application of the Conway-Maxwell-Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes.” Unpublished manuscript.Google Scholar
Machado, J., and J. M. C., Santos Silva. 2005. “Quantiles for Counts.” Journal ofthe American Statistical Association 100: 1226–1237.Google Scholar
Maindonald, J., and J., Braun. 2007. Data Analysis and Graphics UsingR.Cambridge: Cambridge University Press.Google Scholar
McCullagh, P. 1983. “Quasi-likelihood Functions.” Annals of Statistics 11: 59–67.CrossRefGoogle Scholar
McCullagh, P. and J. A., Nelder. 1989. Generalized Linear Models, second edition. New York: Chapman & Hall.CrossRefGoogle Scholar
Miranda, A. 2013. “Un modelo de valla doble para datos de conteo y su aplicación en el estudio de la fecundidad en Mexico,” in Aplicaciones en Economía y Ciencias Sociales con Stata, ed. A., Mendoza. College Station, TX: Stata Press.Google Scholar
Morel, J. G., and N. K., Neerchal. 2012. Overdispersion Models in SAS. Cary, NC: SAS Press.Google Scholar
Muenchen, R., and J. M., Hilbe. 2010. R for Stata Users. New York: Springer.CrossRefGoogle Scholar
Mullahy, J. 1986. “Specification and Testing of Some Modified Count Data Models.” Journal ofEconometrics 33: 341–365.Google Scholar
Nelder, J., and D., Pregibon. 1987. “An Extended Quasi-likelihood Function.” Biometrika 74: 221–232.CrossRefGoogle Scholar
Newbold, E. M. 1927. “Practical Applications of the Statistics of Repeated Events, Particularly to Industrial Accidents.” Journal ofthe Royal Statistical Society 90: 487–547.Google Scholar
Pan, W. 2001. “Akaike's Information Criterion in Generalized Estimating Equations.” Biometrics 57: 120–125.CrossRefGoogle ScholarPubMed
Rabe-Hesketh, S., and A., Skrondal. 2005. Multilevel and Longitudinal Modeling Using Stata.College Station, TX: Stata Press.Google Scholar
Rabe-Hesketh, S., and M., Stasinopoulos. 2008. “A Flexible Regression Approach Using GAMLSS” in R. Handout for a short course in GAMLSS given at International Workshop of Statistical Modelling, University of Utrecht.Google Scholar
Rodriguez-Avi, J., A., Conde-Sanchez, A. J., Saez-Castillo, M. J., Olmo-Jimenez, and A. M., Martínez-Rodríguez. 2009. “A Generalized Waring Regression Model for Count Data.” Computational Statistics and Data Analysis 53 (no. 10): 3717–3725.CrossRefGoogle Scholar
Rouse, D. M. 2005. “Estimation of Finite Mixture Models.” Masters Thesis, Department of Electrical Engineering, North Carolina State University.Google Scholar
Schwarz, G. E. 1978. “Estimating the Dimension of a Model.” Annals of Statistics 6 (no. 2): 461–464.CrossRefGoogle Scholar
Sellers, K. F., S., Borle, and G., Shmueli. 2012. “The COM-Poisson Model for Count Data: A Survey of Methods and Applications.” Applied Stochastic Models in Business and Industry 28 (no. 2).Google Scholar
Sellers, K. F., and G., Shmueli. 2010. “A Flexible Regression Model for Count Data.” Annals ofApplied Statistics 4 (no. 2): 943-961.Google Scholar
Shults, J., and J. M., Hilbe. 2014. Quasi-Least Squares Regression. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Smith, D. M., and M. J., Faddy. “Mean and Variance Modelling of Under- and Over-dispersed Count Data.” Journal of Statistical Software. EPPM.functions Counts. CRAN.
Smithson, M., and E. C., Merkle. 2014. Generalized Linear Models for Categorical and Continuous Limited Dependent Variables. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Stasinopoulos, M., B., Rigby, and C., Akantziliotou. 2008. Instructions on How to Use the gamlss Package in R, second edition. CRAN.Google Scholar
Stone, C. S. 1985. “Additive Regression and Other Nonparametric Models.” Annals of Statistics 13 (no. 2): 689-705.CrossRefGoogle Scholar
Tang, W., H., He, and X. M., Tu. 2013. Applied Categorical and Count Data Analysis. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Tutz, G. 2012. Regression for Categorical Data.Cambridge: Cambridge University Press.Google Scholar
Vickers, A. 2010. What Is a P-Value Anyway?Boston: Addison-Wesley.Google Scholar
Vittinghoff, E., and C. E., McCulloch. 2006. “Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression.” American Journal of Epidemiology 165 (no. 6): 710–718.Google ScholarPubMed
Vuong, Q. H. 1989. “Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses.” Econometrica 57: 307–333.CrossRefGoogle Scholar
Wang, Z. 2000. “Sequential and Drop One Term Likelihood-Ratio Tests.” Stata Technical Bulletin 54: sg133.Google Scholar
Wedderburn, R. W. M. 1974. “Quasi-likelihood Functions, Generalized Linear Models and the Gauss-Newton Method.” Biometrika 61: 439–447.Google Scholar
Westfall, P. H., and K. S. S., Henning. 2013. Understanding Advanced Statistical Methods.Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
White, H. 1980. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica 48 (no. 4): 817–838.CrossRefGoogle Scholar
Winkelmann, R. 2008. Econometric Analysis of Count Data, 5th ed. New York: Springer.Google Scholar
Xekalaki, E. 1983. “The Univariate Generalized Waring Distribution in Relation to Accident Theory: Proneness, Spells or Contagion?Biometrics 39 (no. 3): 39–47.CrossRefGoogle ScholarPubMed
Zhu, F. 2012. “Modeling Time Series of Counts with COM-Poisson INGARCH Models.” Mathematical and Computer Modelling 56 (no. 9): 191–203.CrossRefGoogle Scholar
Zou, Y., S. R., Geedipally, and D., Lord. 2013. “Evaluating the Double Poisson Generalized Linear Model.” Unpublished manuscript.Google ScholarPubMed
Zuur, A. 2012. A Beginner's Guide to Generalized Additive Models with R.Newburgh: Highlands Statistics.Google Scholar
Zuur, A. F., J. M., Hilbe, and E. N., Ieno. 2013. A Beginners Guide to GLM and GLMM with R: A Frequentist and Bayesian Perspective for Ecologists. Newburgh: Highlands Statistics.Google Scholar
Zuur, A. F., A. A., Sveliev, and E. N., Ieno. 2012. Zero Inflated Models and Generalized Linear Mixed Models with R. Newburgh: Highlands Statistics.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • Bibliography
  • Joseph M. Hilbe, Arizona State University
  • Book: Modeling Count Data
  • Online publication: 05 August 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139236065.012
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Bibliography
  • Joseph M. Hilbe, Arizona State University
  • Book: Modeling Count Data
  • Online publication: 05 August 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139236065.012
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Bibliography
  • Joseph M. Hilbe, Arizona State University
  • Book: Modeling Count Data
  • Online publication: 05 August 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139236065.012
Available formats
×