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Nonparametric quantile regression estimation for functional data with responses missing at random

Author

Listed:
  • Dengke Xu

    (Zhejiang Agriculture and Forestry University)

  • Jiang Du

    (Beijing University of Technology)

Abstract

This paper presents the nonparametric quantile regression estimation for the regression function operator when the functional data with the responses missing at random are considered. Then, the large sample properties of the proposed estimator are established under some mild conditions. Finally, a simulation study is conducted to investigate the finite sample properties of the proposed method.

Suggested Citation

  • Dengke Xu & Jiang Du, 2020. "Nonparametric quantile regression estimation for functional data with responses missing at random," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(8), pages 977-990, November.
    • Handle: RePEc:spr:metrik:v:83:y:2020:i:8:d:10.1007_s00184-020-00769-z
    DOI: 10.1007/s00184-020-00769-z
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    References listed on IDEAS

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    1. Wang, Qihua & Sun, Zhihua, 2007. "Estimation in partially linear models with missing responses at random," Journal of Multivariate Analysis, Elsevier, vol. 98(7), pages 1470-1493, August.
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    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. Laib, Naâmane & Louani, Djamal, 2010. "Nonparametric kernel regression estimation for functional stationary ergodic data: Asymptotic properties," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2266-2281, November.
    5. Hua Liang & Suojin Wang & Raymond J. Carroll, 2007. "Partially linear models with missing response variables and error-prone covariates," Biometrika, Biometrika Trust, vol. 94(1), pages 185-198.
    6. Dehan Kong & Arnab Maity & Fang-Chi Hsu & Jung-Ying Tzeng, 2016. "Testing and estimation in marker-set association study using semiparametric quantile regression kernel machine," Biometrics, The International Biometric Society, vol. 72(2), pages 364-371, June.
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