Multiple-index varying-coefficient models for longitudinal data

References

• M. Allon, J. Daugirdas, T.A. Depner, T. Greene, D. Ornt, and S.J. Schwab, for the HEMO Study Group, Effect of change in vascular access on patient mortality in hemodialysis patients, Amer. J. Kidney Dis. 47 (2006), pp. 469–477. doi: 10.1053/j.ajkd.2005.11.023
   PubMed Web of Science ®Google Scholar
• B.C. Astor, J.A. Eustace, N.R. Powe, M.J. Klag, N.E. Fink, and J. Coresh. for the CHOICE Study, Type of vascular access and survival among incident hemodialysis patients: The choices for healthy outcomes in caring for ESRD (CHOICE) study, J. Amer. Soc. Nephrol. 16 (2005), pp. 1449–1455. doi: 10.1681/ASN.2004090748
   PubMed Web of Science ®Google Scholar
• B.D. Bradbury, F. Chen, A. Furniss, R.L. Pisoni, M. Keen, D. Mapes, and M. Krishnan, Conversion of vascular access type among incident hemodialysis patients: Description and association with mortality, Amer. J. Kidney Dis. 53 (2009), pp. 804–814. doi: 10.1053/j.ajkd.2008.11.031
   PubMed Web of Science ®Google Scholar
• Z. Cai, J. Fan, and R. Li, Efficient estimation and inferences for varying-coefficient models, J. Amer. Statist. Assoc. 95 (2000), pp. 888–902. doi: 10.1080/01621459.2000.10474280
   Web of Science ®Google Scholar
• R.K. Dhingra, E.W. Young, T.E. Hulbert-Shearon, S.F. Leavey, and F.K. Port, Type of vascular access and mortality in U.S. hemodialysis patients, Kidney Int. 60 (2001), pp. 1443–1451. doi: 10.1046/j.1523-1755.2001.00947.x
   PubMed Web of Science ®Google Scholar
• J. Fan and I. Gijbels, Local Polynomial Modelling and Its Application, Chapman and Hall, London, 1996.
   Google Scholar
• J. Fan and T. Huang, Profile likelihood inferences on semiparametric varying-coefficient partially linear models, Bernoulli 11 (2005), pp. 1031–1057. doi: 10.3150/bj/1137421639
   Web of Science ®Google Scholar
• J. Fan, T. Huang, and R. Li, Analysis of longitudinal data with semiparametric estimation of covariance function, J. Amer. Statist. Assoc. 102 (2007), pp. 632–641. doi: 10.1198/016214507000000095
   PubMed Web of Science ®Google Scholar
• T. Hastie and R. Tibshirani, Varying-coefficient models, J. R. Statist. Soc. Ser. B 55 (1993), pp. 757–796.
   Google Scholar
• D.R. Hoover, J.A. Rice, C.O. Wu, and L.P. Yang, Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data, Biometrika 85 (1998), pp. 809–822. doi: 10.1093/biomet/85.4.809
   Web of Science ®Google Scholar
• J.Z. Huang, C.O. Wu, and L. Zhou, Varying-coefficient models and basis function approximations for the analysis of repeated measurements, Biometrika 89 (2002), pp. 111–128. doi: 10.1093/biomet/89.1.111
   Web of Science ®Google Scholar
• J.Z. Huang, C.O. Wu, and L. Zhou, Polynomial spline estimation and inference for varying coefficient models with longitudinal data, Statist. Sinica 14 (2004), pp. 763–788.
   Web of Science ®Google Scholar
• C.-R. Jiang and J.-L. Wang, Functional single index models for longitudinal data, Ann. Statist. 39 (2011), pp. 362–388. doi: 10.1214/10-AOS845
   Web of Science ®Google Scholar
• E. Lacson, W. Wang, M. Lazarus, and R.M. Hakim, Change in vascular access and mortality in maintenance hemodialysis patients, Amer. J. Kidney Dis. 54 (2009), pp. 912–921. doi: 10.1053/j.ajkd.2009.07.008
   PubMed Web of Science ®Google Scholar
• C. Leng, W. Zhang, and J. Pan, Semiparametric mean-covariance regression analysis for longitudinal data, J. Amer. Statist. Assoc. 105 (2010), pp. 181–193. doi: 10.1198/jasa.2009.tm08485
   Web of Science ®Google Scholar
• J. Pan and G. Mackenzie, On modelling mean-covariance structures in longitudinal studies, Bimetrika 90 (2003), pp. 239–244. doi: 10.1093/biomet/90.1.239
   Web of Science ®Google Scholar
• S. Pastan, J.M. Soucie, and W.M. McClellan, Vascular access and increased risk of death among hemodialysis patients, Kidney Int. 62 (2002), pp. 620–626. doi: 10.1046/j.1523-1755.2002.00460.x
   PubMed Web of Science ®Google Scholar
• J.C. Pinheiro and D.M. Bates, Mixed-Effects Models in S and S-PLUS, Springer, New York, 2000.
   Google Scholar
• M. Pourahmadi, Joint mean-covariance models with applications to longitudinal data: Unconstrained parameterisation, Bimetrika 86 (1999), pp. 677–690. doi: 10.1093/biomet/86.3.677
   Web of Science ®Google Scholar
• B.W. Silverman and J.T. Wood, The nonparametric estimation of branching curves, J. Amer. Statist. Assoc. 82 (1987), pp. 551–558. doi: 10.1080/01621459.1987.10478465
   Web of Science ®Google Scholar
• U.S. Renal Data System, USRDS 2014 Annual Data Report: Atlas of End-stage Renal Disease in the United States, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2014.
   Google Scholar
• H. Wong, W.-C. Ip, and R.Q. Zhang, Varying-coefficient single-index model, Comput. Statist. Data Anal. 52 (2008), pp. 1458–1476. doi: 10.1016/j.csda.2007.04.008
   Web of Science ®Google Scholar
• W. Yao and R. Li, New local estimation procedure for a non-parametric regression function for longitudinal data, J. R. Statist. Soc. Ser. B 75 (2013), pp. 123–138. doi: 10.1111/j.1467-9868.2012.01038.x
   Google Scholar
• H. Ye and J. Pan, Modelling of covariance structures in generalised estimating equations for longitudinal data, Bimetrika 93 (2006), pp. 927–941. doi: 10.1093/biomet/93.4.927
   Web of Science ®Google Scholar
• R.Q. Zhang and G.Y. Li, Integrated estimation of functional-coefficient regression models with different smoothing variables, Commun. Statist. Theory Methods 35 (2006), pp. 1093–1100. doi: 10.1080/03610920600672260
   Web of Science ®Google Scholar