Semiparametric statistical inferences for longitudinal data with nonparametric covariance modelling

References

• Liang K-L, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73:13–22. doi: 10.1093/biomet/73.1.13
   Web of Science ®Google Scholar
• Verbeke G, Molenberghs G. Linear mixed models for longitudinal data. New York: Springer; 2009.
   Google Scholar
• Lin X, Carroll RJ. Semiparametric regression for clustered data. Biometrika. 2001;88(4):1179–1185. doi: 10.1093/biomet/88.4.1179
   Web of Science ®Google Scholar
• Li Y. Efficient semiparametric regression for longitudinal data with nonparametric covariance estimation. Biometrika. 2011;98(2):355–370. doi: 10.1093/biomet/asq080
   Web of Science ®Google Scholar
• Lin X, Carroll RJ. Semiparametric regression for clustered data using generalized estimating equations. J Am Stat Assoc. 2001;96(455):1045–1056. doi: 10.1198/016214501753208708
   Web of Science ®Google Scholar
• Wang N. Marginal nonparametric kernel regression accounting for within-subject correlation. Biometrika. 2003;90(1):43–52. doi: 10.1093/biomet/90.1.43
   Web of Science ®Google Scholar
• Lin XH, Wang N, Welsh AH, et al. Equivalent kernels of smoothing splines in nonparametric regression for clustered/longitudinal data. Biometrika. 2004;91:177–193. doi: 10.1093/biomet/91.1.177
   Web of Science ®Google Scholar
• Wang N, Carroll RJ, Lin X. Efficient semiparametric marginal estimation for longitudinal/clustered data. J Amer Statist Assoc. 2005;100:147–157. doi: 10.1198/016214504000000629
   Web of Science ®Google Scholar
• Chen K, Jin Z. Local polynomial regression analysis of clustered data. Biometrika. 2005;92(1):59–74. doi: 10.1093/biomet/92.1.59
   Web of Science ®Google Scholar
• Zeger SL, Diggle PJ. Semiparametric models for longitudinal data with application to cd4 cell numbers in hiv seroconverters. Biometrics. 1994;50:689–699. doi: 10.2307/2532783
   PubMed Web of Science ®Google Scholar
• Lin XH, Carroll RJ. Nonparametric function estimation for clustered data when the predictor is measured without/with error. J Amer Statist Assoc. 2000;95:520–534. doi: 10.1080/01621459.2000.10474229
   Web of Science ®Google Scholar
• He X, Zhu ZY, Fung WK. Estimation in a semiparametric model for longitudinal data with unspecified dependence structure. Biometrika. 2002;89(3):579–590. doi: 10.1093/biomet/89.3.579
   Web of Science ®Google Scholar
• Fan J, Li R. New estimation and model selection procedures for semiparametric modeling in longitudinal data analysis. J Am Stat Assoc. 2004;99(467):710–723. doi: 10.1198/016214504000001060
   Web of Science ®Google Scholar
• Diggle PJ, Heagerty P, Liang KY, et al. Analysis of longitudinal data. 2nd ed. New York: Oxford University Press; 2002.
   Google Scholar
• Baltagi BH. Econometrics analysis of panel data. Chichester: Wiley; 2008.
   Google Scholar
• Berhane K, Tibshirani RJ. Generalized additive models for longitudinal data. Can J Stat. 1998;26:517–535. doi: 10.2307/3315715
   Web of Science ®Google Scholar
• You J, Zhou H. Two-stage efficient estimation of longitudinal nonparametric additive models. Stat Probab Lett. 2007;77(17):1666–1675. doi: 10.1016/j.spl.2007.04.006
   Web of Science ®Google Scholar
• Carroll RJ, Maity A, Mammen E, et al. Nonparametric additive regression for repeatedly measured data. Biometrika. 2009;96(2):383–398. doi: 10.1093/biomet/asp015
   Web of Science ®Google Scholar
• Stone CJ. Additive regression and other nonparametric models. Ann Stat. 1985;13:689–705. doi: 10.1214/aos/1176349548
   Web of Science ®Google Scholar
• Fan Y, Li Q. A kernel-based method for estimating additive partially linear models. Statist Sin. 2003;13:739–762.
   Web of Science ®Google Scholar
• Stone CJ. The dimensionality reduction principle for generalized additive models. Ann Stat. 1986;14(2):590–606. doi: 10.1214/aos/1176349940
   Web of Science ®Google Scholar
• Fan J, Härdle W, Mammen E. Direct estimation of low-dimensional components in additive models. Ann Stat. 1998;26:943–971. doi: 10.1214/aos/1024691083
   Web of Science ®Google Scholar
• Li Q. Efficient estimation of additive partially linear models. Int Econ Rev (Philadelphia). 2000;41(4):1073–1092. doi: 10.1111/1468-2354.00096
   Web of Science ®Google Scholar
• Ma S, Song Q, Wang L. Variable selection and estimation in marginal partially linear additive models for longitudinal data. Bernoulli (Andover). 2013;19:252–274. doi: 10.3150/11-BEJ386
   Web of Science ®Google Scholar
• Chiou J-M, Müller H-G. Estimated estimating equations: semiparametric inference for clustered and longitudinal data. J R Statist Soc: Ser B. 2005;67(4):531–553. doi: 10.1111/j.1467-9868.2005.00514.x
   Google Scholar
• Huang JZ, Zhang L, Zhou L. Efficient estimation in marginal partially linear models for longitudinal/clustered data using splines. Scand J Stat. 2007;34(3):451–477. doi: 10.1111/j.1467-9469.2006.00550.x
   Web of Science ®Google Scholar
• Xue L, Qu A, Zhou J. Consistent model selection for marginal generalized additive model for correlated data. J Am Stat Assoc. 2010;105(492):1518–1530. doi: 10.1198/jasa.2010.tm10128
   Web of Science ®Google Scholar
• Fan J, Wu Y. Semiparametric estimation of covariance matrixes for longitudinal data. J Am Stat Assoc. 2008;103(484):1520–1533. doi: 10.1198/016214508000000742
   PubMed Web of Science ®Google Scholar
• Yao F, Müller H-G, Wang J-L. Functional data analysis for sparse longitudinal data. J Am Stat Assoc. 2005;100(470):577–590. doi: 10.1198/016214504000001745
   Web of Science ®Google Scholar
• Hall P, Müller H-G, Wang J-L. Properties of principal component methods for functional and longitudinal data analysis. Ann Stat. 2006;34(3):1493–1517. doi: 10.1214/009053606000000272
   Web of Science ®Google Scholar
• Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. J Am Stat Assoc. 2001;96(456):1348–1360. doi: 10.1198/016214501753382273
   Web of Science ®Google Scholar
• de Boor C. A bound on the l∞-norm of l2-approximation by splines in terms of a global mesh ratio. Math Comput. 1976;30:765–771.
   Web of Science ®Google Scholar
• Schumaker L. Spline functions: basic theory. New York: Wiley; 1981.
   Google Scholar
• Stone CJ. Optimal global rates of convergence for nonparametric regression. Ann Stat. 1982;10:1040–1053. doi: 10.1214/aos/1176345969
   Web of Science ®Google Scholar
• Wang L, Li H, Huang JZ. Variable selection in nonparametric varying-coefficient models for analysis of repeated measurements. J Am Stat Assoc. 2008;103(484):1556–1569. doi: 10.1198/016214508000000788
   PubMed Web of Science ®Google Scholar