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A Penalized Spline Approach to Functional Mixed Effects Model Analysis

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  • Huaihou Chen
  • Yuanjia Wang

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  • Huaihou Chen & Yuanjia Wang, 2011. "A Penalized Spline Approach to Functional Mixed Effects Model Analysis," Biometrics, The International Biometric Society, vol. 67(3), pages 861-870, September.
    • Handle: RePEc:bla:biomet:v:67:y:2011:i:3:p:861-870
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2010.01524.x
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    References listed on IDEAS

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    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, September.
    2. John A. Rice & Colin O. Wu, 2001. "Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves," Biometrics, The International Biometric Society, vol. 57(1), pages 253-259, March.
    3. Fan, Jianqing & Huang, Tao & Li, Runze, 2007. "Analysis of Longitudinal Data With Semiparametric Estimation of Covariance Function," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 632-641, June.
    4. Gerda Claeskens & Tatyana Krivobokova & Jean D. Opsomer, 2009. "Asymptotic properties of penalized spline estimators," Biometrika, Biometrika Trust, vol. 96(3), pages 529-544.
    5. Wei Biao Wu, 2003. "Nonparametric estimation of large covariance matrices of longitudinal data," Biometrika, Biometrika Trust, vol. 90(4), pages 831-844, December.
    6. Jianhua Z. Huang, 2002. "Varying-coefficient models and basis function approximations for the analysis of repeated measurements," Biometrika, Biometrika Trust, vol. 89(1), pages 111-128, March.
    7. Wensheng Guo, 2002. "Functional Mixed Effects Models," Biometrics, The International Biometric Society, vol. 58(1), pages 121-128, March.
    8. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
    9. Sally A. Wood, 2002. "Bayesian mixture of splines for spatially adaptive nonparametric regression," Biometrika, Biometrika Trust, vol. 89(3), pages 513-528, August.
    10. Göran Kauermann & Tatyana Krivobokova & Ludwig Fahrmeir, 2009. "Some asymptotic results on generalized penalized spline smoothing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 487-503, April.
    11. Fang Yao & Thomas C. M. Lee, 2006. "Penalized spline models for functional principal component analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 3-25, February.
    12. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, September.
    13. Robert T. Krafty & Phyllis A. Gimotty & David Holtz & George Coukos & Wensheng Guo, 2008. "Varying Coefficient Model with Unknown Within-Subject Covariance for Analysis of Tumor Growth Curves," Biometrics, The International Biometric Society, vol. 64(4), pages 1023-1031, December.
    14. Ciprian M. Crainiceanu & David Ruppert, 2004. "Likelihood ratio tests in linear mixed models with one variance component," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 165-185, February.
    15. Jianhua Z. Huang & Naiping Liu & Mohsen Pourahmadi & Linxu Liu, 2006. "Covariance matrix selection and estimation via penalised normal likelihood," Biometrika, Biometrika Trust, vol. 93(1), pages 85-98, March.
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    Cited by:

    1. Rakêt, Lars Lau & Markussen, Bo, 2014. "Approximate inference for spatial functional data on massively parallel processors," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 227-240.
    2. Ali M. Mosammam & Jorge Mateu, 2018. "A penalized likelihood method for nonseparable space–time generalized additive models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(3), pages 333-357, July.
    3. Xiong Cai & Liugen Xue & Xiaolong Pu & Xingyu Yan, 2021. "Efficient Estimation for Varying-Coefficient Mixed Effects Models with Functional Response Data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(4), pages 467-495, May.
    4. Ruiyan Luo & Xin Qi, 2023. "Nonlinear function‐on‐scalar regression via functional universal approximation," Biometrics, The International Biometric Society, vol. 79(4), pages 3319-3331, December.
    5. Daniel Gervini & Patrick A. Carter, 2014. "Warped functional analysis of variance," Biometrics, The International Biometric Society, vol. 70(3), pages 526-535, September.
    6. Pan, Qing & Zhao, Yunpeng, 2016. "Integrative weighted group lasso and generalized local quadratic approximation," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 66-78.
    7. Yihao Li & Danh V. Nguyen & Esra Kürüm & Connie M. Rhee & Yanjun Chen & Kamyar Kalantar‐Zadeh & Damla Şentürk, 2020. "A multilevel mixed effects varying coefficient model with multilevel predictors and random effects for modeling hospitalization risk in patients on dialysis," Biometrics, The International Biometric Society, vol. 76(3), pages 924-938, September.
    8. Moiseev, Nikita & Volodin, Andrei, 2019. "Increasing the accuracy of macroeconomic time series forecast by incorporating functional and correlational dependencies between them," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 53, pages 119-137.

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