References
- Breslow, N. E., and D. G. Clayton. 1993. Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88:9–25. doi:https://doi.org/10.2307/2290687.
- Cho, H., P. Wang, and A. Qu. 2017. Personalize treatment for longitudinal data using unspecified random-effects model. Statistica Sinica 27:187–205. doi:https://doi.org/10.5705/ss.202015.0120.
- Diaz, F. J., T. E. Rivera, R. C. Josiassen, and J. Leon. 2007. Individualizing drug dosage by using a random intercept linear model. Statistics in Medicine 26 (9):2052–73. doi:https://doi.org/10.1002/sim.2636.
- Diaz, F. J., H. W. Yeh, and J. Leon. 2012. Role of statistics random-effects linear models in personalized medicine. Current Pharmacogenomics and Personalized Medicine 10:22–32. doi:https://doi.org/10.2174/1875692111201010022.
- Fang, S., H. Zhang, and L. Sun. 2016. Joint analysis of longitudinal data with additive mixed effect model for informative observation times. Journal of Statistical Planning and Inference 169:43–55. doi:https://doi.org/10.1016/j.jspi.2015.08.001.
- Jiang, B. J. 1999. Conditional inference about generalized linear mixed models. The Annals of Statistics 27 (6):1974–2007. doi:https://doi.org/10.1214/009053605000000543.
- Jiang, J., and W. Zhang. 2001. Robust estimation in generalized linear mixed models. Biometrika 88 (3):753–65. doi:https://doi.org/10.1093/biomet/88.3.753.
- Laird, N. M., and J. H. Ware. 1982. Random-effects models for longitudinal data. Biometrics 38 (4):963–74.
- McCulloch, C. E. 1997. Maximum likelihood algorithm for generalized linear mixed models. Journal of the American Statistical Association 92 (437):162–70. doi:https://doi.org/10.2307/2291460.
- McCulloch, C. E., S. R. Searle, and J. M. Neuhaus. 2008. Generalized, linear, and mixed models. 2nd ed. New York, NY: John Wiley.
- Ortega, J. M., and W. C. Rheinboldt. 1973. Iterative solutions of nonlinear equations in several variables. Cambridge, MA: Academic Press.
- Vonesh, E. F., H. Wang, L. Nie, and D. Majumdar. 2002. Conditional second-order generalized estimating equations for generalized linear and nonlinear mixed-effects models. Journal of the American Statistical Association 97 (457):271–83. doi:https://doi.org/10.1198/016214502753479400.
- Wang, N. 2003. Marginal nonparametric kernel regression accounting for within-subject correlation. Biometrika 90 (1):43–52. doi:https://doi.org/10.1093/biomet/90.1.43.
- Wang, Y. G., and V. Carey. 2003. Working correlation structure misspecification, estimation and covariate design: Implications for generalized estimating equations performance. Biometrika 90 (1):29–41. doi:https://doi.org/10.1093/biomet/90.1.29.
- Wang, P., G. F. Tsai, and A. Qu. 2012. Conditional inference functions for mixed-effects models with unspecified random-effects distribution. Journal of the American Statistical Association 107 (498):725–36. doi:https://doi.org/10.1080/01621459.2012.665199.
- Wedderburn, R. W. 1974. Quasi-likelihood functions, generalized linear models and the gauss-newton method. Biometrika 61:439–47. doi:https://doi.org/10.2307/2334725.
- Westgate, PM. 2012. A bias-corrected covariance estimate for improved inference with quadratic inference functions. Statistics in Medicine 31 (29):4003–22. doi:https://doi.org/10.1002/sim.5479.
- Westgate, PM. 2013. A bias correction for covariance estimators to improve inference with generalized estimating equations that use an unstructured correlation matrix. Statistics in Medicine 32 (16):2850–8. doi:https://doi.org/10.1002/sim.5709.
- Xing, Y. C., L. L. Xu, W. Q. Ma, and Z. C. Zhu. 2018. Conditional mix-GEE models for longitudinal data with unspecified random-effects distributions. Communications in Statistics - Theory and Methods 47 (4):862–76. doi:https://doi.org/10.1080/03610926.2016.1267763.
- Xu, L. L., N. Lin, B. X. Zhang, and N. Z. Shi. 2012. A finite mixture model for working correlation matrices in generalized estimating equations. Statistica Sinica 22 (2):755–76. doi:https://doi.org/10.5705/ss.2010.090.