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Varying coefficient partially functional linear regression models

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  • Qing-Yan Peng

    (Yunnan University)

  • Jian-Jun Zhou

    (Yunnan University)

  • Nian-Sheng Tang

    (Yunnan University)

Abstract

By relaxing the linearity assumption in partial functional linear regression models, we propose a varying coefficient partially functional linear regression model (VCPFLM), which includes varying coefficient regression models and functional linear regression models as its special cases. We study the problem of functional parameter estimation in a VCPFLM. The functional parameter is approximated by a polynomial spline, and the spline coefficients are estimated by the ordinary least squares method. Under some regular conditions, we obtain asymptotic properties of functional parameter estimators, including the global convergence rates and uniform convergence rates. Simulation studies are conducted to investigate the performance of the proposed methodologies.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stpapr:v:57:y:2016:i:3:d:10.1007_s00362-015-0681-3
    DOI: 10.1007/s00362-015-0681-3
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    References listed on IDEAS

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    Cited by:

    1. Liebl, Dominik & Walders, Fabian, 2019. "Parameter regimes in partial functional panel regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 105-115.
    2. Ruiyuan Cao & Jiang Du & Jianjun Zhou & Tianfa Xie, 2020. "FPCA-based estimation for generalized functional partially linear models," Statistical Papers, Springer, vol. 61(6), pages 2715-2735, December.
    3. Ping Yu & Zhongyi Zhu & Zhongzhan Zhang, 2019. "Robust exponential squared loss-based estimation in semi-functional linear regression models," Computational Statistics, Springer, vol. 34(2), pages 503-525, June.
    4. Guodong Shan & Yiheng Hou & Baisen Liu, 2020. "Bayesian robust estimation of partially functional linear regression models using heavy-tailed distributions," Computational Statistics, Springer, vol. 35(4), pages 2077-2092, December.

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