The COUNTREG Procedure

References

  • Abramowitz, M. and Stegun, A. (1970), Handbook of Mathematical Functions, New York: Dover Press.

  • Amemiya, T. (1985), Advanced Econometrics, Cambridge, MA: Harvard University Press.

  • Cameron, A. C. and Trivedi, P. K. (1986), “Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests,” Journal of Applied Econometrics, 1, 29–53.

  • Cameron, A. C. and Trivedi, P. K. (1998), Regression Analysis of Count Data, Cambridge: Cambridge University Press.

  • Conway, R. W. and Maxwell, W. L. (1962), “A Queuing Model with State Dependent Service Rates,” Journal of Industrial Engineering, 12, 132–136.

  • Fan, J. and Li, R. (2001), “Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties,” Journal of the American Statistical Association, 96, 1348–1360.

  • Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2004), Bayesian Data Analysis, 2nd Edition, London: Chapman & Hall.

  • Godfrey, L. G. (1988), Misspecification Tests in Econometrics, Cambridge: Cambridge University Press.

  • Greene, W. H. (1994), “Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models,” Working paper 94-10, Leonard N. Stern School of Business, Department of Economics, New York University.
    URL http://ideas.repec.org/p/ste/nystbu/94-10.html

  • Greene, W. H. (2000), Econometric Analysis, 4th Edition, Upper Saddle River, NJ: Prentice-Hall.

  • Guikema, S. D. and Coffelt, J. P. (2008), “A Flexible Count Data Regression Model for Risk Analysis,” Risk Analysis, 28, 213–223.

  • Hausman, J. A., Hall, B. H., and Griliches, Z. (1984), “Econometric Models for Count Data with an Application to the Patents-R&D Relationship,” Econometrica, 52, 909–938.

  • King, G. (1989a), “A Seemingly Unrelated Poisson Regression Model,” Sociological Methods and Research, 17, 235–255.

  • King, G. (1989b), Unifying Political Methodology: The Likelihood Theory and Statistical Inference, Cambridge: Cambridge University Press.

  • Lambert, D. (1992), “Zero-Inflated Poisson Regression with an Application to Defects in Manufacturing,” Technometrics, 34, 1–14.

  • LaMotte, L. R. (1994), “A Note on the Role of Independence in t Statistics Constructed from Linear Statistics in Regression Models,” American Statistician, 48, 238–240.

  • Long, J. S. (1997), Regression Models for Categorical and Limited Dependent Variables, Thousand Oaks, CA: Sage Publications.

  • Lord, D., Guikema, S. D., and Geedipally, S. R. (2008), “Application of the Conway-Maxwell-Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes,” Accident Analysis and Prevention, 40, 1123–1134.

  • Park, M. Y. and Hastie, T. J. (2007), “$l_11$-Regularization Path Algorithm for Generalized Linear Models,” Journal of the Royal Statistical Society, Series B, 69, 659–677.

  • Roberts, G. O., Gelman, A., and Gilks, W. R. (1997), “Weak Convergence and Optimal Scaling of Random Walk Metropolis Algorithms,” Annals of Applied Probability, 7, 110–120.

  • Roberts, G. O. and Rosenthal, J. S. (2001), “Optimal Scaling for Various Metropolis-Hastings Algorithms,” Statistical Science, 16, 351–367.

  • Schervish, M. J. (1995), Theory of Statistics, New York: Springer-Verlag.

  • Searle, S. R. (1971), Linear Models, New York: John Wiley & Sons.

  • Shmueli, G., Minka, T. P., Kadane, J. B., Borle, S., and Boatwright, P. (2005), “A Useful Distribution for Fitting Discrete Data: Revival of the Conway-Maxwell-Poisson Distribution,” Journal of the Royal Statistical Society, Series C, 54, 127–142.

  • Winkelmann, R. (2000), Econometric Analysis of Count Data, Berlin: Springer-Verlag.

  • Zou, H. and Li, R. (2008), “One-Step Sparse Estimates in Nonconcave Penalized Likelihood Models,” Annals of Statistics, 36, 1509–1533.