Estimation and variable selection for partially functional linear models |
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Authors: | Jiang Du Dengke Xu Ruiyuan Cao |
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Institution: | 1. College of Applied Sciences, Beijing University of Technology, Beijing, 100124, China;2. Department of Statistics, Zhejiang Agriculture and Forestry University, Hangzhou, 311300, China |
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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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Keywords: | 62G05 62G20 Partially functional linear regression model Variable selection Composite quantile regression Oracle property |
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