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Estimation for generalized partially functional linear additive regression model

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  • Jiang Du
  • Ruiyuan Cao
  • Eddy Kwessi
  • Zhongzhan Zhang

Abstract

In practice, it is not uncommon to encounter the situation that a discrete response is related to both a functional random variable and multiple real-value random variables whose impact on the response is nonlinear. In this paper, we consider the generalized partial functional linear additive models (GPFLAM) and present the estimation procedure. In GPFLAM, the nonparametric functions are approximated by polynomial splines and the infinite slope function is estimated based on the principal component basis function approximations. We obtain the estimator by maximizing the quasi-likelihood function. We investigate the finite sample properties of the estimation procedure via Monte Carlo simulation studies and illustrate our proposed model by a real data analysis.

Suggested Citation

  • Jiang Du & Ruiyuan Cao & Eddy Kwessi & Zhongzhan Zhang, 2019. "Estimation for generalized partially functional linear additive regression model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(5), pages 914-925, April.
    • Handle: RePEc:taf:japsta:v:46:y:2019:i:5:p:914-925
    DOI: 10.1080/02664763.2018.1523378
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    Cited by:

    1. Zhu, Hanbing & Zhang, Yuanyuan & Li, Yehua & Lian, Heng, 2023. "Semiparametric function-on-function quantile regression model with dynamic single-index interactions," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    2. Římalová, Veronika & Fišerová, Eva & Menafoglio, Alessandra & Pini, Alessia, 2022. "Inference for spatial regression models with functional response using a permutational approach," Journal of Multivariate Analysis, Elsevier, vol. 189(C).

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