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FPCA-based estimation for generalized functional partially linear models

Author

Listed:
  • Ruiyuan Cao

    (Beijing University of Technology)

  • Jiang Du

    (Beijing University of Technology)

  • Jianjun Zhou

    (Yunnan University)

  • Tianfa Xie

    (Beijing University of Technology)

Abstract

In real data analysis, practitioners frequently come across the case that a discrete response will be related to both a function-valued random variable and a vector-value random variable as the predictor variables. In this paper, we consider the generalized functional partially linear models (GFPLM). The infinite slope function in the GFPLM is estimated by the principal component basis function approximations. Then, we consider the theoretical properties of the estimator obtained by maximizing the quasi likelihood function. The asymptotic normality of the estimator of the finite dimensional parameter and the rate of convergence of the estimator of the infinite dimensional slope function are established, respectively. We investigate the finite sample properties of the estimation procedure via Monte Carlo simulation studies and a real data analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stpapr:v:61:y:2020:i:6:d:10.1007_s00362-018-01066-8
    DOI: 10.1007/s00362-018-01066-8
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    References listed on IDEAS

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    Cited by:

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    2. Mohamed Alahiane & Idir Ouassou & Mustapha Rachdi & Philippe Vieu, 2021. "Partially Linear Generalized Single Index Models for Functional Data (PLGSIMF)," Stats, MDPI, vol. 4(4), pages 1-21, September.

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