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Efficient Estimation for Varying-Coefficient Mixed Effects Models with Functional Response Data

Author

Listed:
  • Xiong Cai

    (Beijing University of Technology)

  • Liugen Xue

    (Beijing University of Technology)

  • Xiaolong Pu

    (East China Normal University)

  • Xingyu Yan

    (East China Normal University)

Abstract

In this article, we focus on the estimation of varying-coefficient mixed effects models for longitudinal and sparse functional response data, by using the generalized least squares method coupling a modified local kernel smoothing technique. This approach provides a useful framework that simultaneously takes into account the within-subject covariance and all observation information in the estimation to improve efficiency. We establish both uniform consistency and pointwise asymptotic normality for the proposed estimators of varying-coefficient functions. Numerical studies are carried out to illustrate the finite sample performance of the proposed procedure. An application to the white matter tract dataset obtained from Alzheimer’s Disease Neuroimaging Initiative study is also provided.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:metrik:v:84:y:2021:i:4:d:10.1007_s00184-020-00776-0
    DOI: 10.1007/s00184-020-00776-0
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    References listed on IDEAS

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