References
- B.A. Brumback, Z. Cai, Z. He, H.W. Zhenga, and A.B. Dailey, Conditional pseudolikelihood methods for clustered ordinal, multinomial, or count outcomes with complex survey data, Stat. Med. 32 (2013), pp. 1325–1335. doi: 10.1002/sim.5625
- W.S. Comulada and R.E. Weiss, On models for binomial data with random numbers of trials, Biometrics 63 (2007), pp. 610–617. doi: 10.1111/j.1541-0420.2006.00722.x
- I.D. Ha and Y. Lee, Comparison of hierarchical likelihood versus orthodox best linear unbiased predictor approaches for frailty models, Biometrika 92 (2005), pp. 717–723. doi: 10.1093/biomet/92.3.717
- P.J. Heagerty and S.L. Zeger, Marginalized multilevel models and likelihood inference (with discussion), Statist. Sci. 15 (2000), pp. 1–26.
- D. Karlis, An EM algorithm for multivariate Poisson distribution and related models, J. Appl. Stat. 30 (2003), pp. 63–77. doi: 10.1080/0266476022000018510
- M.I. Lora and J.M. Singer, Beta-binomial/Poisson regression models for repeated bivariate counts, Stat. Med. 27 (2008), pp. 3366–3381. doi: 10.1002/sim.3303
- M.I. Lora and J.M. Singer, Beta-binomial/gamma-Poisson regression models for repeated counts with random parameters, Braz. J. Probab. Stat. 25 (2011), pp. 218–235. doi: 10.1214/10-BJPS118
- R. Ma and B. Jørgensen, Nested generalized linear mixed models: An orthodox best linear unbiased predictor approach, J. R. Statist. Soc. B 69 (2007), pp. 625–641. doi: 10.1111/j.1467-9868.2007.00603.x
- R. Ma, B. Jørgensen, and J. D. Willms, Clustered binary data with random cluster sizes: A dual Poisson modelling approach, Statist. Model. 9 (2009), pp. 137–150. doi: 10.1177/1471082X0800900203
- J.M. Neuhaus and C.E. McCulloch, Estimation of covariate effects in generalized linear mixed models with informative cluster sizes, Biometrika 98 (2011), pp. 147–162. doi: 10.1093/biomet/asq066
- Office of Technology Assessment, U.S. Congress, Environmental Contaminants in Food, Library of Congress catalog card number 79–600207, 1979.
- O. Sauzet, K.C. Wright, L. Marston, P. Brocklehurst, and J.L. Peacock, Modelling the hierarchical structure in datasets with very small clusters: A simulation study to explore the effect of the proportion of clusters when the outcome is continuous, Stat. Med. 32 (2013), pp. 1429–1438. doi: 10.1002/sim.5638
- T.R. Ten Have and V.M. Chinchilli, Two-stage negative binomial and overdispersed Poisson models for clustered developmental toxicity data with random cluster size, J. Agric. Biol. Environ. Stat. 3 (1998), pp. 75–98. doi: 10.2307/1400624
- P. Zhang, Model selection via multifold cross validation, Ann. Stat. 21 (1993), pp. 299–313. doi: 10.1214/aos/1176349027
- J. Zhu, J.C. Eickhoff, and M.S. Kaiser, Modeling the dependence between number of trials and success probability in beta-binomial-Poisson mixture distributions, Biometrics 59 (2003), pp. 955–961. doi: 10.1111/j.0006-341X.2003.00110.x
- W. Zucchini and I.L. MacDonald, Hidden Markov Models for Time Series – An Introduction using R, Boca Raton, FL, Chapman and Hall, 2009.