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CountDataBiblio.doc 2005, Timothy G. Gregoire, Yale University http://www.yale.edu/forestry/gregoire/downloads/stats/countdatabiblio.pdf Last revised: August 25, 2005 Count Data Bibliography 1. Min, Y. & A. Agresti (2005) Random effect models for repeated measures of zero-inflated count data Statistical Modeling, 5: 1-19. 2. Fletcher, D., D. MacKenzie, and E. Villouta. (2005) Modelling skewed data with many zeroes: a simple approach combining ordinary and logistic regression Environmental and Ecological Statistics, 12: 45-54. 3. Waarton, D. (2005) Many zeros does not mean zero inflation: com- paring the goodness-of-fit of parametric models to multivariate abundance data Environmetrics, 16:275-289. 4. Ferrari, S. & F. Cribari-Neto (2004) Beta Regression for Modelling Rates and Proportions Journal of Applied Statistics, 31:7: 799-815. 5. Hall, D. B. and Z. Zhang. (2004) Marginal models for zero-inflated clustered data Statistical Modelling, 4:161-180. 6. Ugarte, M.D., B. Ibanez, & A.F. Militino. (2004) Testing for Poisson Zero Inflation in Disease Mapping Biometrical Journal, 46:5, 526-539. 7. Poston, D.L. Jr. & S.L.McKibben. (2003) Using Zero-inflated Count Regression Models To Estimate The Fertility of U.S. Women Journal of Modern Applied Statistical Methods, Vol. 2, #2, 371-379. 8. Astuti, E.T. l& T. Yanagawa. (2002) Testing Trend for Count Data with Extra-Poisson Variability Biometrics, 58 (2), pp. -. 9. King, G. (2002) COUNT: A Program for Estimating Event Count and Duration Regressions. 10. Min, Y. & A. Agresti. (2002) Modeling Nonnegative Data with Clumping at Zero: A Survey Journal of the Iranian Statistical Society, pp. 1-30. 11. Podlich, H., M. Faddy, & G. Smyth. (2002) A General Approach to Modeling and Analysis of Species Abundance Data With Extra Zeros Journal of Agricultural, Biological, and Environmental Statistics, Vol. 7, # 3, pp. 324-334. 12. Agresti, A. & R. Natarajan. (2001) Modeling Clustered Ordered Categorical Data: A Survey International Statistical Review, pp. 1-56.

2 13. Dobbie, M. & A. Welsh. (2001) Models for zero-inflated count data using the Neyman type A distribution Statistical Modelling, 1:65-80. 14. Faddy, M. & R.Bosch (2001) Likelihood-Based Modeling and Analysis of Data Underdispersed Relative to the Poisson Distribution Biometrics, 57:620-624. 15. Moore, D., C.Park, & W. Smith. (2001) Exploring Extra-Binomial Variation in Teratology Data Using Continuous Mixtures Biometrics, 57:490-494. 16. Lee, Y. & J.A. Nelder. (2000) Two ways of modeling overdispersion in non-normal data Applied Statistics, 49 (part 4) pp. 591-598. 17. Slaton, T.L., W.W. Piegorsch & S.D. Durham. (2000) Estimation and Testing with Overdispersed Proportions Using the Beta-Logistic Regression Model of Heckman and Willis Biometrics 56:125-133. 18. Brandt, P. & J. Williams. (1999) Time Series Models for Event Count Data. pp. 1-40. 19. Dai, J. & D.Rocke. (1999) Modeling Spatial Variation in Area Source Emissions Journal of Agricultural, Biological and Environmental Statistics, Vol. 5, # 1, pp. 7-114. 20. Gumpertz, M.L., C. Wu & J.M. Pye. (1999) Logistic Regression for Southern Pine Beetle Outbreaks with Spatial and Temporal Autocorrelation Forest Science, Vol. 46, #1, pp. 95 -. 21. King, G., O. Rosen & M. Tanner. (1999) Binomial-Beta Hierarchical Models for Ecological Inference Sociological Methods & Research, Vol. 28, # 1, pp. 61-90. 22. Lindsey, J.K. (1999) Response Surfaces for Overdispersion in the Study of the Conditions for Fish Eggs Hatching Biometrics, 55:149-155. 21. Rao, J.N.K. & A.J. Scott. (1999) A Simple Method for Analysing Overdispersion in Clustered Poisson Data Statistics in Medicine, 18:1373-1385. 22. Tempelman, R.J. & D. Gianola. (1999) Genetics and Breeding Journal of Dairy Science, 82:1834-1847. 23. Wiens, B.L. (1999) When Log-Normal and Gamma Models Give Different Results: A Case Study American Statistician, Vol. 53, #2, pp. 89-93. 24. Young, L., N.Campbell & G. Capuano. (1999) Analysis of Overdispersed Count Data from Single-Factor Experiments: A Comparative Study Journal of Agricultural, Biological & Environmental Statistics, Vol. 4, #3, 258-275.

3 25. Have, T.R.T. & V.M. Chinchilli. (1998) Two-Stage Negative Binomial and Overdispersed Poisson Models for Clustered Developmental Toxicity Data with Random Cluster Size Journal of Agricultural, Biological & Environmental Statistics, Vol. 3, #1, pp. 75-98. 26. Mullahy, J. (1998) Much ado about two: reconsidering retransformation and the two-part model in health econometrics Journal of Health Economics, 17:247-281. 27. Fitzmaurice, G.M., A.F. Heath & D.R. Cox. (1997) Detecting Overdispersion in Large Scale Surveys: Application to a Study of Education and Social Class in Britain Applied Statistics, 46, #4, pp. 415-432. 28. Mullahy, J. (1997) Heterogeneity, Excess Zeros, and the Structure of Count Data Models Journal of Applied Econometrics, 12:337-350. 29. Mullahy, J. (1997) Instrumental-Variable Estimation of Count Data Models: Applications To Models Of Cigarette Smoking Behavior The Review of Economics and Statistics,, pp. 586-593. 30. Aitkin, M. (1996) A general maximum likelihood analysis of overdispersion in generalized linear models Statistics & Computing, 6:251-262. 31. Piegorsch, W.W. & G. Casella. (1996) Empirical Bayes Estimation for Logistic Regression and Extended Parametric Regression Models Journal of Agricultural, Biological & Environmental Statistics, Vol. 1, #2, pp. 231-247. 32. Tempelman, R.J. & D. Gianola. (1996) A Mixed Effects Model for Over- Dispersed Count Data in Animal Breeding Biometrics, 52:265 279. 33. Welsh, A.H., R.Cunningham, C.Donnelly, & D. Lindenmayer. (1996) Modelling the abundance of rare species: statistical models for counts with extra zeros Ecological Modelling, 88:297-308. 34. White, G.C. & R. Bennetts. (1995) Analysis of Frequency Count Data Using Negative Binomial Distribution Ecology, 77(8) pp. 2549-2557. 35. Luceno, A. (1995) A family of partially correlated Poisson models for overdispersion Computational Statistics & Data Analysis, 20: 511-520. 36. Congdon, P. (1994) Spatiotemporal analysis of area mortality The Statistician, 43, #4, pp. 513-528. 37. Gaylor, D.W. (1994) Dose Response Modeling Development Toxicology, 2 nd Ed., New York: Raven Press.

4 38. Haseman, J.K. & W.W Piegorsch. (1994) Statistical Analysis of Developmental Toxicity Data Development Toxicology, 2 nd Ed., New York: Raven Press. 39. Liang, K-Y & J. Hanfelt. (1994) On the Use of the Quasi-Likelihood Method in Teratological Experiments Biometrics, 50: 872-880. 40. Boos, D. (1993) Analysis of Dose-Response Data in the Presence of Extrabinomial Variation Applied Statistics, Vol. 42, # 1, pp. 173-183. 41. Liang. L-Y & P. McCullagh. (1993) The Consultant s Forum: Case Studies in Binary Dispersion Biometrics, 49, 623-630. 42. Dean, C. (1992) Testing for Overdispersion in Poisson and Binormal Models Journal of the American Statistical Association, Vol. 87, #418, pp. 451-457. 43. Morgan, B. (1992) Analysis of Quantal Response Data London: Chapman and Hall (QH 323.5 M67X). 44. Piegorsch, W.W. (1992) Complementary Log Regression for Generalized Linear Models The American Statistician, Vol. 46, #2, pp. 94-99. 45. Grogger, J. & R. Carson. (1991) Models for Truncated Counts Journal of Applied Econometrics, 6:225-238. 46. Kodell, R.L., R.B. Howe, J.J. Chen & D.W. Gaylor. (1991) Mathematical Modeling of Reproductive and Developmental Toxic Effects for Quantitative Risk Assessment Risk Analysis, Vol. 11, #4, pp. 583-590. 47. Seaman, J. & R.Jaeger (1990) Statisticae Dogmaticae: A Critical Essay on Statistical Practice in Ecology Herpetologica, 46(3), pp. 337-346. 48. Cameron, A. & P.Trivedi. (1990) Regression-based Tests for Overdispersion in the Poisson Model Journal of Econometrics, 46:347-364. 49. Dean, C. & J.Lawless (1989) Tests for Detecting Overdispersion in Poisson Regression Models Journal of the American Statistical Association, Vol. 84, # 406, pp. 467-472. 50. King, G. (1989) Variance Specification in Even Count Models: From Restrictive Assumptions to a Generalized Estimator American Journal of Political Science, Vol. 33, No.3, pp. 762-784.

5 51. King, G. (1989) A Seemingly Unrelated Poisson Regression Model Sociological Methods & Research, Vol. 17, #3, 235-255. 52. King, G. (1989) Event Count Models for International Relations: Generalizations and Applications International Studies Quarterly, 33:123-147. 53. Firth, D. (1988) Multiplicative Errors: Log-normal or Gamma? Journal of. the Royal Statistical Society, 50, #2, pp. 266-268. 54. Zeger, S.L. (1998) A regression model for time series of counts Biometrika 75, #4, pp. 621-629. 55. Morton, R. (1987) A generalized linear model with nested strata of extra- Poisson variation Biometrika, 74, #2, pp. 247-257. 56. Williams, D.A. (1987) Dose Response Models for Teratological Experiments Biometrics, 43:1013-1016. 57. Cameron, A.C. & P.Trivedi. (1986) Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests Journal of Applied Econometrics, Vol. 1, issue 1, pp. 29-53. 58. Lee, L-F. (1986) Specification Test for Poisson Regression Models International Economic Review, Vol. 27, # 3, pp. 689-706. 59. Mullahy, J. (1986) Specification and Testing of Some Modified Count Data Models Journal of Econometrics, 33:341-365. 60. Johnson, & Katz (19 ) Poisson Distribution Enc of Statistics, Chapter 4, pp. 87-121. 61. Rai, K. & J. Van Ryzin. (1985) A Dose-Response Model for Teratological Experiments Involving Quantal Responses Biometrics, 41, 1-9. 62. Esterby, S.R. & A.H. El-Shaarawi. (1984) Coliform concentrations in Lake Erie 1966-1970 Hydrobiologia, 111, pp. 133-146. 63. Breslow, N. (1984) Extra-Poisson Variation in Log-linear Models Applied Statistics, Vol. 33, # 1, pp. 38-44. 64. Gourieroux, A. A. Monfort, & A. Trognon. (1984) Pseudo Maximums Likelihood Methods: Applications to Poisson Models Econometrica, Vol. 52, #3, pp. 701-720. 65. Cox, D.R. (1982) Some remarks on overdispersion Biometrika, 70, #1,

6 pp. 269-274. 66. Frome, E. (1983) The Analysis of Rates Using Poisson Regression Models Biometrics, 39:665-674. 67. Diggle, P. (1982) Some Statistical Aspects of Spatial Distribution Models for Plants and Trees Studia Forestalia Suecica, #162, 1-47. 68. Williams, D.A. (1982) Extra-binomial Variation in Logistic Linear Models Applied Stataistics, 31, #2, pp. 144-148. 69. White, G. (1980) Statistical Analysis of Deer and Elk Pellet-Group Data Journal of Wildlife Management, 44(1) pp. 121-131. 70. Altham, P.M. (1978) Two Generalizations of the Binomial Distribution Applied Statistics, 27 (#2, pp. 162-167. 71. Kupper, L.L. & J.K. Haseman. (1978) The Use of a Correlated Binomial Model for the Analysis of Certain Toxicological Experiments Biometrics, 34, 69-76. 72. Williams, D.A. (1975) The Analysis of Binary Responses from Toxicological Experiments Involving Reproduction and Teratogenicity Biometrics, 31: 949-952. 73. Griffiths, D.A. (1973) Maximum Likelihood Estimation for the Beta- Binomial Distribution and an Application to the Household Distribution of the Total Number of Cases of a Disease Biometrics, 29: 637-648. 74. Kleinman, J. (1973) Proportions with Extraneous Variance: Single and Independent Samples Journal of the American Statistical Association, Vol. 68, # 341, pp. 46-54. 75. Anscombe, F.J. (1949) Note on a Problem in Probit Analysis The Annals of Applied Biology, Vol. 136, pp. 203-205. 76. Cochran, W. (1943) Analysis of Variance for Percentages Based on Unequal Numbers Journal of the American Statistical Association, Vol. 38, Issue 223, pp. 287-301. 77. Clapham, A.R. (1936) Over-Dispersion in Grassland Communities and the Use of Statistical Methods in Plant Ecology Journal of Ecology, 24: 232-251.

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