Hierarchical time-varying mixed-effects models in high-dimensional time series and longitudinal data studies

References

• Berry, A.C. (1941), ‘The Accuracy of the Gaussian Approximation to the Sum of Independent Variates’, Transactions of the American Mathematical Society, 49(1), 122–136. doi: 10.1090/S0002-9947-1941-0003498-3
   Google Scholar
• Brown, J (2014), 2014: The year that nothing worked. Fortune, http://fortune.com/2014/12/24/investing-year-review/.
   Google Scholar
• Cai, Z., Fan, J., and Yao, Q. (2000), ‘Functional-coefficient Regression Models for Nonlinear Time Series’, Journal of the American Statistical Association, 95(451), 941–956. doi: 10.1080/01621459.2000.10474284
   Web of Science ®Google Scholar
• Dette, H. (1999), ‘A Consistent Test for the Functional Form of a Regression Based on a Difference of Variance Estimators’, The Annals of Statistics, 27, 1012–1040. doi: 10.1214/aos/1018031266
   Web of Science ®Google Scholar
• Fan, J., and Gijbels, I. (1996), Local Polynomial Modelling and Its Applications: Monographs on Statistics and Applied Probability (Vol. 66), Boca Raton, FL: CRC Press.
   Google Scholar
• Fan, J., Huang, T., and Li, R. (2007), ‘Analysis of Longitudinal Data with Semiparametric Estimation of Covariance Function’, Journal of the American Statistical Association, 102, 632–641. doi: 10.1198/016214507000000095
   PubMed Web of Science ®Google Scholar
• Fan, J., and Wu, Y. (2008), ‘Semiparametric Estimation of Covariance Matrices for Longitudinal Data’, Journal of the American Statistical Association, 103, 1520–1533. doi: 10.1198/016214508000000742
   PubMed Web of Science ®Google Scholar
• Fan, J., and Zhang, J. (2000), ‘Two-step Estimation of Functional Linear Models with Applications to Longitudinal Data’, Journal of the Royal Statistical Society: Series B, 62, 303–322. doi: 10.1111/1467-9868.00233
   Google Scholar
• Field, C.A., and Welsh, A.H. (2007), ‘Bootstrapping Clustered Data’, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(3), 369–390. doi: 10.1111/j.1467-9868.2007.00593.x
   Web of Science ®Google Scholar
• Hall, P., Horowitz, J.L., and Jing, B.Y. (1995), ‘On Blocking Rules for the Bootstrap with Dependent Data’, Biometrika, 82(3), 561–574. doi: 10.1093/biomet/82.3.561
   Web of Science ®Google Scholar
• Heagerty, P., and Zeger, S. (2000), ‘Marginalized Multilevel Models and Likelihood Inference’, Statistics Science, 15, 1–26.
   Web of Science ®Google Scholar
• Hoover, D.R., Rice, J.A., Wu, C.O., and Yang, L.P. (1998), ‘Nonparametric Smoothing Estimates of Time-varying Coefficient Models with Longitudinal Data’, Biometrika, 85(4), 809–822. doi: 10.1093/biomet/85.4.809
   Web of Science ®Google Scholar
• Huang, J.Z., Wu, C.O., and Zhou, L. (2002), ‘Varying-coefficient Models and Basis Function Approximations for the Analysis of Repeated Measurements’, Biometrika, 89(1), 111–128. doi: 10.1093/biomet/89.1.111
   Web of Science ®Google Scholar
• Hull, J.C. (2006), Options, Futures, and Other Derivatives, Upper Saddle River, NJ: Pearson Education India.
   Google Scholar
• Lin, X., and Carroll, R.J. (2000), ‘Nonparametric Function Estimation for Clustered Data when the Predictor is Measured Without/with Error’, Journal of the American Statistical Association, 95, 520–534. doi: 10.1080/01621459.2000.10474229
   Web of Science ®Google Scholar
• Lin, X., and Carroll, R.J. (2001), ‘Semiparametric Regression for Clustered Data Using Generalized Estimating Equations’, Journal of the American Statistical Association, 96, 1045–1056. doi: 10.1198/016214501753208708
   Web of Science ®Google Scholar
• Liu, L., Ma, J., and O'Quigley, J. (2008), ‘Joint Analysis of Multi-level Repeated Measures Data and Survival: An Application to the End Stage Renal Disease (ESRD) Data’, Statistics in Medicine, 27, 5679–5691. doi: 10.1002/sim.3392
   PubMed Web of Science ®Google Scholar
• Oravecz, Z., Tuerlinckx, F., and Vandekerckhove, J. (2009), ‘A Hierarchical Ornstein–Uhlenbeck Model for Continuous Repeated Measurement Data’, Psychometrika, 74(3), 395–418. doi: 10.1007/s11336-008-9106-8
   Web of Science ®Google Scholar
• Ren, S., Lai, H., Tong, W., Aminzadeh, M., Hou, X., and Lai, S. (2010), ‘Nonparametric Bootstrapping for Hierarchical Data’, Journal of Applied Statistics, 37(9), 1487–1498. doi: 10.1080/02664760903046102
   Web of Science ®Google Scholar
• Verbeke, G., and Molenberghts, G. (2000), Linear Mixed Models for Longitudinal Data, New York: Springer-Verlag.
   Google Scholar
• Wang, N., Carroll, R.J., and Lin, X. (2005), ‘Efficient Semiparametric Marginal Estimation for Longitudinal/clustered Data’, Journal of the American Statistical Association, 100, 147–157. doi: 10.1198/016214504000000629
   Web of Science ®Google Scholar
• Wang, Y., and Taylor, J.M.G. (2001), ‘Jointly Modeling Longitudinal and Event Time Data with Application to Acquired Immunodeficiency Syndrome’, Journal of the American Statistical Association, 96(455), 895–905. doi: 10.1198/016214501753208591
   Web of Science ®Google Scholar
• Wu, H., and Zhang, J. (2002), ‘Local Polynomial Mixed-effects Models for Longitudinal Data’, Journal of the American Statistical Association, 97, 833–897.
   Web of Science ®Google Scholar