Distribution-free inference of zero-inflated binomial data for longitudinal studies

References

• G. Chamberlain, Asymptotic efficiency in estimation with conditional moment restrictions, J. Econometrics 34 (1987), pp. 305–334. doi: 10.1016/0304-4076(87)90015-7
   Web of Science ®Google Scholar
• COMBINE Study Research Group, Testing combined pharmacotherapies and behavioral interventions in alcohol dependence: Rationale and methods, Alcoholism (NY) 27 (2003), pp. 1107–1122.
   Google Scholar
• COMBINE Study Research Group, Combined pharmacotherapies and behavioral interventions for alcohol dependence – the COMBINE Study: A randomized controlled trial, JAMA 295 (2006), pp. 2003–2017.
   Google Scholar
• M. Crowder, On linear and quadratic estimating functions, Biometrika 74 (1987), pp. 591–597. doi: 10.1093/biomet/74.3.591
   Web of Science ®Google Scholar
• M.J. Dobbie and A.H. Welsh, Modelling correlated zero-inflated count data, Aust. N. Z. J. Stat. 43 (2001), pp. 431–444. doi: 10.1111/1467-842X.00191
   Web of Science ®Google Scholar
• E.W. Freesm and E. Valdez, Understanding relationships using copulas, N. Am. Actuar. J. 2 (1998), Pp. 1–25. doi: 10.1080/10920277.1998.10595667
   Google Scholar
• D.B. Hall, Zero-inflated Poisson and binomial regression with random effects: A case study, Biometrics 56 (2000), pp. 1030–1039. doi: 10.1111/j.0006-341X.2000.01030.x
   PubMed Web of Science ®Google Scholar
• D.B. Hall and Z. Zhang, Marginal models for zero inflated clustered data, Stat. Model. 4 (2004), pp. 161–180. doi: 10.1191/1471082X04st076oa
   Web of Science ®Google Scholar
• J. Kowalski and X.M. Tu, Modern Applied U-Statistics, Wiley Series in Probability and Statistics, Wiley-Interscience, Hoboken, NJ, 2008.
   Google Scholar
• J. Kowalski, X.M. Tu, R.S. Day, and J. Mendoza-Blanco , On the rate of convergence of the ECME algorithm for multiple regression models with t-distributed errors, Biometrika 84 (1997), pp. 269–281. doi: 10.1093/biomet/84.2.269
   Web of Science ®Google Scholar
• D. Lambert, Zero-inflated Poisson regression, with an application to defects in manufacturing, Technometrics 34 (1992), pp. 1–14. doi: 10.2307/1269547
   Web of Science ®Google Scholar
• K.-Y. Liang and S.L. Zeger, Longitudinal data analysis using generalized linear models, Biometrika 73 (1986), pp. 13–22. doi: 10.1093/biomet/73.1.13
   Web of Science ®Google Scholar
• T. Louis, Finding the observed information matrix when using the EM algorithm, J. R. Stat. Soc. Ser. B Methodol. 44 (1982), pp. 226–233.
   Web of Science ®Google Scholar
• Y. Ma, W. Tang, C. Feng, and X.M. Tu , Inference for Kappas for longitudinal study data: Applications to sexual health research, Biometrics 64 (2008), pp. 781–789. doi: 10.1111/j.1541-0420.2007.00934.x
   PubMed Web of Science ®Google Scholar
• I. Meilijson, A fast improvement to the EM algorithm on its own terms, J. R. Stat. Soc. Ser. B Methodol. 51 (1989), pp. 127–138.
   Google Scholar
• X. Meng and D. Rubin, Using EM to obtain asymptotic variance–covariance matrices: The SEM algorithm, J. Amer. Statist. Assoc. 86 (1991), pp. 899–909. doi: 10.1080/01621459.1991.10475130
   Web of Science ®Google Scholar
• R.B. Nelsen, An Introduction to Copulas, Springer, New York, NY, 2006.
   Google Scholar
• W.K. Newey, Adaptive estimation of regression models via moment restrictions, J. Econometrics 38 (1988), pp. 301–339. doi: 10.1016/0304-4076(88)90048-6
   Web of Science ®Google Scholar
• R.L. Prentice and L.P. Zhao, Estimating equations for parameters in means and covariances of multivariate discrete and continuous responses, Biometrics (1991), pp. 825–839. doi: 10.2307/2532642
   PubMed Web of Science ®Google Scholar
• M. Ridout, J. Hinde, and C.G. DemeAtrio, A score test for testing a zero-inflated poisson regression model against zero-inflated negative binomial alternatives, Biometrics 57 (2001), pp. 219–223. doi: 10.1111/j.0006-341X.2001.00219.x
   PubMed Web of Science ®Google Scholar
• J.M. Robins and A. Rotnitzky, Semiparametric efficiency in multivariate regression models with missing data, J. Amer. Statist. Assoc. 90 (1995), pp. 122–129. doi: 10.1080/01621459.1995.10476494
   Web of Science ®Google Scholar
• D.B. Rubin, Inference and missing data, Biometrika 63 (1976), pp. 581–592. doi: 10.1093/biomet/63.3.581
   Web of Science ®Google Scholar
• W. Tang, H. He, and D. Gunzler, Kernel smoothing density estimation when group membership is subject to missing, J. Statist. Plann. Inference 142 (2012), pp. 685–694. doi: 10.1016/j.jspi.2011.09.009
   PubMed Web of Science ®Google Scholar
• W. Tang, H. He, and X. Tu, Applied Categorical and Count Data Analysis, Chapman & Hall/CRC, New York, NY, 2012.
   Google Scholar
• A.A. Tsiatis, Semiparametric Theory and Missing Data, Springer Series in Statistics, Springer, New York, 2006.
   Google Scholar
• A.M.C. Vieira, J.P. Hinde, and C.G.B. Demétrio, Zero-inflated proportion data models applied to a biological control assay, J. Appl. Stat. 27 (2000), pp. 373–389. doi: 10.1080/02664760021673
   Web of Science ®Google Scholar
• C. Wu, On the convergence properties of the EM algorithm, Ann. Statist. 11 (1983), pp. 95–103. doi: 10.1214/aos/1176346060
   Web of Science ®Google Scholar
• J. Yan, Enjoy the joy of copulas: With a package copula, J. Stat. Softw. 21 (2007), pp. 1–21.
   Google Scholar
• Q. Yu, R. Chen, W. Tang, H. He, R. Gallop, P. Crits-Christoph, J. Hu, and X.M. Tu, Distribution-free models for longitudinal count responses with overdispersion and structural zeros, Stat. Med. 32 (2013), pp. 2390–2405. doi: 10.1002/sim.5691
   PubMed Web of Science ®Google Scholar