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Composite quantile estimation in partial functional linear regression model with dependent errors

Author

Listed:
  • Ping Yu

    (Fudan University
    Shanxi Normal University)

  • Ting Li

    (Fudan University)

  • Zhongyi Zhu

    (Fudan University)

  • Zhongzhan Zhang

    (Beijing University of Technology)

Abstract

In this paper, we consider composite quantile estimation for the partial functional linear regression model with errors from a short-range dependent and strictly stationary linear processes. The functional principal component analysis method is employed to estimate the slope function and the functional predictive variable, respectively. Under some regularity conditions, we obtain the optimal convergence rate of the slope function, and the asymptotic normality of the parameter vector. Simulation studies demonstrate that the proposed new estimation method is robust and works much better than the least squares based method when there are outliers in the dataset or the autoregressive error distribution follows a heavy-tailed distribution. Finally, we apply the proposed methodology to electricity consumption data.

Suggested Citation

  • Ping Yu & Ting Li & Zhongyi Zhu & Zhongzhan Zhang, 2019. "Composite quantile estimation in partial functional linear regression model with dependent errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(6), pages 633-656, August.
    • Handle: RePEc:spr:metrik:v:82:y:2019:i:6:d:10.1007_s00184-018-0699-3
    DOI: 10.1007/s00184-018-0699-3
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    References listed on IDEAS

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    Cited by:

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    2. Bin Yang & Min Chen & Tong Su & Jianjun Zhou, 2023. "Robust Estimation for Semi-Functional Linear Model with Autoregressive Errors," Mathematics, MDPI, vol. 11(2), pages 1-14, January.

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