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Two-Stage Functional Mixed Models for Evaluating the Effect of Longitudinal Covariate Profiles on a Scalar Outcome

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  • Daowen Zhang
  • Xihong Lin
  • MaryFran Sowers

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  • Daowen Zhang & Xihong Lin & MaryFran Sowers, 2007. "Two-Stage Functional Mixed Models for Evaluating the Effect of Longitudinal Covariate Profiles on a Scalar Outcome," Biometrics, The International Biometric Society, vol. 63(2), pages 351-362, June.
    • Handle: RePEc:bla:biomet:v:63:y:2007:i:2:p:351-362
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2006.00713.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.
    2. Xihong Lin, 2004. "Equivalent kernels of smoothing splines in nonparametric regression for clustered/longitudinal data," Biometrika, Biometrika Trust, vol. 91(1), pages 177-193, March.
    3. 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.
    4. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506.
    5. Gareth M. James, 2002. "Generalized linear models with functional predictors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 411-432, August.
    6. Daowen Zhang & Xihong Lin & MaryFran Sowers, 2000. "Semiparametric Regression for Periodic Longitudinal Hormone Data from Multiple Menstrual Cycles," Biometrics, The International Biometric Society, vol. 56(1), pages 31-39, March.
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    Cited by:

    1. Qing-Yan Peng & Jian-Jun Zhou & Nian-Sheng Tang, 2016. "Varying coefficient partially functional linear regression models," Statistical Papers, Springer, vol. 57(3), pages 827-841, September.
    2. Dhiman Bhadra & Michael J. Daniels & Sungduk Kim & Malay Ghosh & Bhramar Mukherjee, 2012. "A Bayesian Semiparametric Approach for Incorporating Longitudinal Information on Exposure History for Inference in Case–Control Studies," Biometrics, The International Biometric Society, vol. 68(2), pages 361-370, June.
    3. Qingguo Tang & Peng Jin, 2019. "Estimation and variable selection for partial functional linear regression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(4), pages 475-501, December.
    4. Jianjun Zhou & Zhao Chen & Qingyan Peng, 2016. "Polynomial spline estimation for partial functional linear regression models," Computational Statistics, Springer, vol. 31(3), pages 1107-1129, September.
    5. Zhu, Hanbing & Zhang, Riquan & Yu, Zhou & Lian, Heng & Liu, Yanghui, 2019. "Estimation and testing for partially functional linear errors-in-variables models," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 296-314.
    6. Hanyu Yang & Runze Li & Robert A. Zucker & Anne Buu, 2016. "Two-stage model for time varying effects of zero-inflated count longitudinal covariates with applications in health behaviour research," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(3), pages 431-444, April.
    7. Liebl, Dominik & Walders, Fabian, 2019. "Parameter regimes in partial functional panel regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 105-115.
    8. Shin, Hyejin & Lee, Myung Hee, 2012. "On prediction rate in partial functional linear regression," Journal of Multivariate Analysis, Elsevier, vol. 103(1), pages 93-106, January.

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