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CPS2110 Johann Sebastian B. C. et al.
               null  hypothesis  is  true,  thus  resulting  in  a  poorly-sized  test.  With  the
               advancements in research and computing power, one approach to balance
               the shortcomings of these closed-form variance models is to let go of the
               parameters and consider the nonparametric estimation of the variance. This
               approach can be seen in Claveria (2016).
                   Finally, an improvement to the shortcomings of the current practice of the
               regression-based test for overdispersion is to seek out improvements in the
               test statistic itself. Such is the study of Dean (1992), Baksh et al. (2011), and
               Novo and Manteiga (2000).

               References
               1.  Baksh, F., Böhning, D., & Lerdsuwansri, R. (2011). An extension of an
                   over-dispersion test for count data. Computational Statistics and Data
                   Analysis 55, 466-474.
               2.  Cameron, A., & 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.
               3.  Cameron, A., & Trivedi, P. K. (1990). Regression-Based Test for
                   Overdispersion in the Poisson Model. Journal of Econometrics 34, 347-
                   364.
               4.  Cameron, A., & Trivedi, P. K. (1998). Regression Analysis of Count Data.
                   New York: Cambridge University Press.
               5.  Casella, G., & Berger, R. L. (2002). Statistical Inference, 2nd ed. Duxbury.
               6.  Claveria, J. B. (2016). A Nonparametric Regression-Based Test for Poisson
                   Overdispersion. Quezon City: University of the Philippines.
               7.  Davison, A., & Hinkley, D. V. (1997). Bootstrap Methods and their
                   Application. Cambridge University Press.
               8.  Dean, C. B. (1992). Testing for Overdispersion in Poisson and Binomial
                   Regression Models. Journal of the American Statistical Association 87,
                   451-457.
               9.  Greene, W. (2008). Functional forms for the negative binomial model for
                   count data. Economics Letters 99, 585-590.
               10.  Karlin, S., & Taylor, H. M. (1975). A First Course in Stochastic Processes,
                   2nd ed. New York: Academic Press, Inc.
               11.  Ramil Novo, L., & Gonzalez Manteiga, W. (2000). F Tests and Regression
                   Analysis Based on Smoothing Spline Estimators. Statistica Sinica 10, 819-
                   837.
               12.  Wasserman, L. (2006). All of Nonparametric Statistics. Springer: New
                   York.






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