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Estimation and variable selection for partially functional linear models

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Abstract

In this paper, a new estimation procedure based on composite quantile regression and functional principal component analysis (PCA) method is proposed for the partially functional linear regression models (PFLRMs). The proposed estimation method can simultaneously estimate both the parametric regression coefficients and functional coefficient components without specification of the error distributions. The proposed estimation method is shown to be more efficient empirically for non-normal random error, especially for Cauchy error, and almost as efficient for normal random errors. Furthermore, based on the proposed estimation procedure, we use the penalized composite quantile regression method to study variable selection for parametric part in the PFLRMs. Under certain regularity conditions, consistency, asymptotic normality, and Oracle property of the resulting estimators are derived. Simulation studies and a real data analysis are conducted to assess the finite sample performance of the proposed methods.

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Authors and Affiliations

  1. College of Applied Sciences, Beijing University of Technology, Beijing, 100124, China

    Jiang Du & Ruiyuan Cao

  2. Department of Statistics, Zhejiang Agriculture and Forestry University, Hangzhou, 311300, China

    Dengke Xu

Authors
  1. Jiang Du
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  2. Dengke Xu
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  3. Ruiyuan Cao
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Correspondence to Ruiyuan Cao.

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Du, J., Xu, D. & Cao, R. Estimation and variable selection for partially functional linear models. J. Korean Stat. Soc. 47, 436–439 (2018). https://doi.org/10.1016/j.jkss.2018.05.002

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  • DOI: https://doi.org/10.1016/j.jkss.2018.05.002

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