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.
© 2005 Timothy G. Gregoire
CountDataBiblio.doc
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.
© 2005 Timothy G. Gregoire
CountDataBiblio.doc
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.
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.
27.
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 OverDispersed 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,
2nd Ed., New York: Raven Press.
© 2005 Timothy G. Gregoire
CountDataBiblio.doc
4
38.
Haseman, J.K. & W.W Piegorsch. (1994) “Statistical Analysis of Developmental Toxicity Data” Development Toxicology, 2nd 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.
© 2005 Timothy G. Gregoire
CountDataBiblio.doc
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 extraPoisson 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.
© 2005 Timothy G. Gregoire