ZIP and RelatedBinaryBiblio.pdf
© 2009, Timothy G. Gregoire, Yale University
Last revised: December 2009
ZIP and Related Binary
(104 entries)
Articles:
1. Clapham, A.R. 1936. Over-dispersion in grassland communities and the use of
statistical methods in plant ecology. J. Ecology 24: 232 – 251.
2. Cochran, W.G. (1943). Analysis of variance for percentages based on unequal
numbers. Journal of the American Statistical Association 38(223): 287 – 301.
3. Anscombe, F.J. 1949. Note on a problem in probit analysis. The Annals of Applied
Biology 36: 203 – 205.
4. 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.
5. Kleinman, J.C. 1973. Proportions with extraneous variance: single and
independent samples. Journal of the American Statistical Association 68(341): 46
– 54.
6. Williams, D.A. 1975. The analysis of binary responses from toxicological
experiments involving reproduction and teratogenicity. Biometrics 31: 949 – 952.
7. Altham, P.M.E. 1978. Two generalizations of the binomial distribution. Appl.
Statist. 27(2): 162 – 167.
8. Kupper, L.L. and Haseman, J.K. 1978. The use of a correlated binomial model for
the analysis of certain toxicological experiments. Biometrics 34: 69 – 76.
9. Johnson and Kotz. (
). Poisson Distribution. Chapter 4.
10. White, G.C. 1980. Statistical analysis of deer and elk pellet-group data. J. Wildl.
Manage. 44(1): 121 – 131.
11. Diggle, P. 1982. Some statistical aspects of spatial distribution models for plants
and trees. Studia Forestalia Suecica 162: 47 p.
12. Williams, D.A. 1982. Extra-binomial variation in logistic linear models. Appl.
Statist. 31(2): 144 – 148.
13. Cox, D.R. 1983. Some remarks on overdispersion. Biometrika 70(1): 269 – 274.
© 2009 Timothy G. Gregoire
ZIP and RelatedBinaryBiblio.pdf
© 2009, Timothy G. Gregoire, Yale University 2
14. Frome, E.L. 1983. The analysis of rates using Poisson regression models.
Biometrics 39: 665 – 674.
15. Breslow, N.E. 1984. Extra-Poisson variation in log-linear models. Appl. Statist.
33(1): 38 – 44.
16. Esterby, S.R. and El-Shaarawi, A.H. 1984. Coliform concentrations in Lake Erie
– 1966 to 1970. Hydrobiologia 111: 133 – 146.
17. Gourieroux, C., Monfort, A., Trognon, A. 1984. Pseudo maximum likelihood
methods: applications to Poisson models. Econometrica 52(3): 701 – 720.
18. Rai, K. and Ryzin, J. V. 1985. A dose-response model for teratological
experiments involving quantal responses. Biometrics 41: 1 – 9.
19. 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.
20. King, G. (1986). Statistical models for political science even counts: Bias in
conventional procedures and evidence for the exponential Poisson regression
model*. Havard University.
21. Lee, L. (1986). Specification test for Poisson regression models. International
Economic Review 27(3): 689 – 706.
22. Mullahy, J. (1986). Specification and testing of some modified count data models.
Journal of Econometrics 33: 341 – 365.
23. Morton, R. 1987. A generalized linear model with nested strata of extra-Poisson
variation. Biometrika 74(2): 247 – 257.
24. Williams, D.A. 1987. Dose-response models for teratological experiments
(Reader reaction). Biometrics 43: 1013 – 1016.
25. Firth, D. 1988. Multiplicative errors: log-normal or gamma? J.R. Statist. Soc. B.
50(2): 266 – 268.
26. Zeger, S.L. 1988. A regression model for time series of counts. Biometrika 75(4):
621 – 629.
27. Dean, C. and Lawless, J.F. 1989. Tests for detecting overdispersion in Poisson
regression models. Journal of the American Statistical Association 84(406): 467 –
472.
© 2009 Timothy G. Gregoire
ZIP and RelatedBinaryBiblio.pdf
© 2009, Timothy G. Gregoire, Yale University 3
28. King, G. (1989A). A seemingly unrelated Poisson Regression Model.
Sociological Methods and Research 17(3): 235 – 255.
29. King, G. (1989B). Event count models for international relations: generalizations
and applications. International Studies Quarterly 33: 123 – 147.
30. King, G. (1989C). Variance specification in event count models: from restrictive
assumptions to a generalized estimator. American Journal of Political Science
33(3): 762 – 784.
31. Beta-binomial derivations (1990).
32. ColinCameron, A. and Trivedi, P.K. 1990. Regression-based tests for
overdispersion in the Poisson model. Journal of Econometrics 46: 347 – 364.
33. Seaman, J.W. Jr. and Jaeger, R.G. 1990. Statisticae Dogmaticae: a critical essay
on statistical practice in ecology. Herpetologica 46(3): 337 – 346.
34. Grogger, J.T. and Carson, R.T. 1991. Models for truncated counts. Journal of
Applied Econometrics 6: 225 – 238.
35. Kodell, R.L., Howe, R.B., Chen, J.J., and Gaylor, D.W. 1991. Mathematical
modeling of reproductive and developmental toxic effects for quantitative risk
assessment. Risk Analysis 11(4): 583 – 589.
36. Dean, C.B. 1992. Testing for overdispersion in Poisson and Binomial regression
models. Journal of the American Statistical Association 87(418): 451 – 457.
37. Morgan, B.J.T. 1992. Analysis of quantal response data. QH 323.5 M67X.
38. Piegorsch, W.W. 1992. Complimentary log regression for generalized linear
models. The American Statistician 46(2): 94 – 99.
39. Boos, D.D. 1993. Analysis of dose-response data in the presence of extrabinomial
variation. Appl. Statist. 42(1): 173 – 183.
40. Liang, K. and McCullagh, P. 1993. The Consultant’s Forum: Case studies in
Binary dispersion. Biometrics 49: 623 – 630.
41. Congdon, P. 1994. Spatiotemporal analysis of area mortality. The Statistician
43(4): 513 – 528.
42. Gaylor, D.W. 1994. Dose-response modeling. In Development Toxicology (C.A.
Kimmel & Buelke-Sam, J. eds.). Raven Press Ltd., New York
© 2009 Timothy G. Gregoire
ZIP and RelatedBinaryBiblio.pdf
© 2009, Timothy G. Gregoire, Yale University 4
43. Haseman, J.K. and Piegorsch, W.W. 1994. Statistical Analysis of Developmental
Toxicity data. In Development Toxicology (C.A. Kimmel & Buelke-Sam, J. eds.).
Raven Press Ltd., New York.
44. Liang, K. and Hanfelt, J. 1994. On the use of quasi-likelihood method in
teratological experiments. Biometrics 50: 872 – 880.
45. Luceno, A. 1995. A family of partially correlated Poisson models for
overdispersion. Computational Statistics and Data Analysis 20: 511 – 520.
46. Aitkin, M. 1996. A general maximum likelihood analysis of overdispersion in
generalized linear models. Statistics and Computing 6: 251 – 262.
47. Piegorsch, W.W. and Casella, G. 1996. Emperical Bayes estimation for logistic
regression and extended parametric regression models. Journal of Agricultural,
Biological, and Environmental Statistics 1(2): 231 – 247.
48. Tempelman, R.J. and Gianola, D. 1996. A mixed effects model for overdispersed
count data in animal breeding. Biometrics 52: 265 – 279.
49. Welsh, A.H., Cunningham, R.B., Donnelly, C.F., and Lindenmayer, D.B. 1996.
Modelling the abundance of rare species: statistical models for counts with extra
zeros. Ecological Modelling 88: 297 – 308.
50. White, G.C. and Bennets, R.E. 1996. Analysis of frequency count data using the
negative binomial distribution. Ecology 77(8): 2549 – 2557.
51. Deb, P. and Trivedi, P.K. (1997). Demand for medical care by the elderly: a finite
mixture approach. Journal of Applied Econometrics 12: 313 – 336.
52. Fitzmaurice, G.M., Heath, A.F., and Cox, D.R. 1997. Detecting overdispersion in
large scale surveys: application to a study of education and social class in Britain.
Appl. Statist. 46(4): 415 – 432.
53. Mullahy, J. (1997A). Heterogeneity, excess zeros, and the structure of count data
models. Journal of Applied Econometrics 12: 337 – 350.
54. Mullahy, J. (1997B). Instrumental-variable estimation of count data models:
applications to models of cigarette smoking behavior. The Review of Economics
and Statistics: 586 – 593.
55. Mullahy, J. 1998. Much ado about two: reconsidering retransformation and the
two-part model in health econometrics. Journal of Health Economics 17: 247 –
281.
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ZIP and RelatedBinaryBiblio.pdf
© 2009, Timothy G. Gregoire, Yale University 5
56. Ten Have, T.R. and Chinchilli, V.M. 1998. Two-stage negative binomial and
overdispersed Poisson models for clustered developmental toxicity data with
random cluster size. Journal of Agricultural, Biological, and Environmental
Statistics 3(1): 75 – 98.
57. Brandt, P.T. and Williams, J.T. (1999). Event Count Models. Department of
political science, Indiana University, Bloomington.
58. Gilliland, D. and Schabenberger, O. (1999). Limits on pairwise association in
equi-correlated binomial models. Michigan State University.
59. Gregoire, T. (1999). Correspondence with Oliver Schabenberge.
60. King, G. 1999. Binomial-beta hierarchical models for ecological inference.
Sociological Methods and Research 28(1): 61- 90.
61. Lindsey, J.K. 1999. Response surfaces for overdispersion in the study of the
conditions for fish eggs hatching. Biometrics 55: 149 – 155.
62. Rao, J.N.K. and Scott, A.J. 1999. A simple method for analyzing overdispersion
in clustered Poisson data. Statistics in Medicine 18: 1373 – 1385.
63. Tempelman, R.J. and Gianola, D. (1999). Genetic Analysis of fertility in dairy
cattle using negative binomial mixed models. J. Dairy Sci. 82: 1834 – 1847.
64. Wiens, B.L. 1999. When log-normal and Gamma models give different results: a
case study. The American Statistician 53(2): 89 – 93.
65. Young, L.J., Campbell, N.L., and Capuano, G.A. (1999). Analysis of
overdispersed count data from single-factor experiments: a comparative study.
Journal of Agricultural, Biological, and Environmental Statistics 4(3): 258 – 275.
66. ………(1999). Overdispersion models for counts.
67. Bernoulli Sampling (2000).
68. Gumpertz, M.L., Wu, C., and Pye, J.M. (2000). Logistic regression for Southern
pine beetle outbreaks with spatial and temporal autocorrelation. Forest Science
46(1): 95 –
69. Lee, Y. and Nelder, J.A. (2000). Two ways of modeling overdispersion in nonnormal data. Appl. Statist. 49(4): 591 – 598.
70. Slaton, T.L., Piegorsch, W.W., and Durham, S.D. (2000). Estimation and testing
with overdispersed proportions using the beta-logistic regression model of
Heckman and Willis. Biometrics 56: 125 – 133.
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71. Agresti, A. and Natarajan, R. (2001). Modeling clustered ordered categorical data:
a survey. ISR: 1 – 56.
72. Dobbie, M.J. and Welsh, A.H. (2001). Models for zero-inflated count data using
the Neyman type A distribution. Statistical Modeling 1: 65 -80.
73. Faddy, M.J. and Bosch, R.J. (2001). Likelihood-based modeling and analysis of
data underdispersed relative to the Poisson distribution. Biometrics 57: 620 – 624.
74. Moore, D.F., Park, C.K., and Smith, W. (2001). Exploring extra-binomial
variation in teratology data using continuous mixtures. Biometrics 57: 490 – 494.
75. Astuti, E.T. and Yanagawa, T. (2002). Testing trend for count data with extraPoisson variability. Biometrics 58(2): 1- 17.
76. Gregoire, T. (2002). Correspondence with Oliver Schabenberge.
77. Min, Y. and Agresti, A. (2002). Modeling nonnegative data with clumping at
zero: a survey. J. Iranian Statistical Society: 1 – 30.
78. Podlich, H.M., Faddy, M.J., and Smyth, G.K. (2002). A general approach to
modeling and analysis of species abundance data with extra zeros. Journal of
Agricultural, Biological, and Environmental Statistics 7(3): 324 – 334.
79. King, G. (2002). A program for estimating event count and duration regressions.
Department of Government, Havard University.
80. Poston, D.L. Jr. and McKibben, S.L. (2003). Using zero-inflated count regression
models to estimate the fertility of U.S. women. Journal of Modern Applied
Statistical Methods 2(2): 371 – 379.
81. Ferrari, S.P. and Cribari-Neto, F. (2004). Beta regression for modeling rates and
proportions. Journal of Applied Statistics 31(7): 799 – 815.
82. Hall, D.B. and Zhang, Z. (2004). Marginal models for zero inflated clustered data.
Statistical Modeling 4: 161 – 180.
83. Ugarte, M., Ibanez, B., and Militino, A.F. (2004). Testing for Poisson zero
inflation in disease mapping. Biometrical Journal 46(5): 526 – 539.
84. Fletcher, D., Mackenzie, D., and Villouta, E. (2005). Modelling skewed data with
many zeros: A simple approach combining ordinary and logistic regression.
Environmental and Ecological Statistics 12: 45 – 54.
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ZIP and RelatedBinaryBiblio.pdf
© 2009, Timothy G. Gregoire, Yale University 7
85. Gray, B.R. (2005). Selecting a distributional assumption for modeling relative
densities of benthic macroinvertebrates. Ecological Modelling 185: 1-12.
86. Joe, H. and Zhu, R. (2005). Generalized Poisson distribution: the property of
mixture of Poisson and comparison with negative binomial distribution.
Biometrical Journal 47(2): 219 – 229.
87. Jung, B.C., Jhun, M., and Lee, J.W. (2005). Bootstrap tests for overdispersion in a
zero-inflated Poisson regression model. (Reader Reaction). Biometrics 61: 626 –
629.
88. Karlis, D. and Xekalaki, E. (2005). Mixed Poisson distributions. International
Statistical Review 73(1): 35 – 58.
89. Min, Y. and Agresti, A. (2005). Random effect models for repeated measures of
zero-inflated count data. Statistical Modelling 5: 1 – 19.
90. Rose, C.E., Martin, S.W., Wannemuehler, K.A., and Plikaytis, B.D. (2005). On
the use of zero-inflated and hurdle models for modeling vaccine adverse event
count data. (Manuscript).
91. Warton, D.I. (2005). Many zeros does not mean zero inflation: comparing the
goodness-of-fit of parametric models to multivariate abundance data.
Environmetrics 16: 275 – 289.
92. Affleck, D. L. R. (2006). Poisson mixture models for regression analysis of standlevel mortality. Can. J. For. Res. 36: 2994 – 3006.
93. Famoye, F. and Singh, K. (2006). Zero-inflated generalized Poisson regression
model with an application to domestic violence data. Journal of Data Science 4:
117 – 130.
94. Lam, K.F., Xue, H., and Cheung, Y.B. (2006). Semiparametric analysis of zeroinflated count data. Biometrics 62: 996 – 1003.
95. Loehle, C. (2006). Climate change in the context of long-term geological data. In
Focus on Ecological Research. 1-39.
96. Rathbun, S.L. and Fei, S. (2006). A spatial zero-inflated poisson regression model
for oak regeneration. Environ Ecol Stat 13: 409 – 426.
97. Thomas, A. (2006). Consistent estimation of binary-choice panel data models
with heterogeneous linear trends. Econometrics Journal 9: 177 – 195.
98. Demidenko, E. (2007). Poisson regression for clustered data. International
Statistical Review 75 (1): 96 – 113.
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99. Fortin, M. and DeBlois, J. (2007). Modeling tree recruitment with zero-inflated
models: The example of hardwood stands in Southern Quebec, Canada. Forest
Science 53(4): 529.
100.
Ver Hoef, J.M. and Jansen, J.K. (2007). Space-time zero-inflated count
models of Harbor seals. Environmetrics 18: 697 – 712.
101.
Finley, A.O., Banerjee, S., and Walters, B. (2008). A hierarchical mixture
model with remotely sensed predictors for estimating zero inflated
continuous forest variables.
102.
Moghimbeigi, A., Eshraghian, M. R., Mohammad, K., and McArdle, B.
(2008). Multilevel zero-inflated negative binomial regression modeling for
over-dispersed count data with extra zeros. Journal of Applied Statistics
35(10): 1193 – 1202.
103.
Ramsey, D. (2008). Glmer and overdispersed Poisson models (upgrade of
lme4).
104.
Lee, J., Jung, B.C., and Jin, S.H. (2009). Tests for zero inflation in a
bivariate zero-inflated Poisson model. Statistica Neerlandica 63(4): 400 –
417.
© 2009 Timothy G. Gregoire