Estimation and variable selection for partial functional linear regression

Qingguo Tang () and Peng Jin
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Qingguo Tang: Nanjing University of Science and Technology
Peng Jin: Nanjing University of Science and Technology

AStA Advances in Statistical Analysis, 2019, vol. 103, issue 4, No 2, 475-501

Abstract: Abstract We propose a new estimation procedure for estimating the unknown parameters and function in partial functional linear regression. The asymptotic distribution of the estimator of the vector of slope parameters is derived, and the global convergence rate of the estimator of unknown slope function is established under suitable norm. The convergence rate of the mean squared prediction error for the proposed estimators is also established. Based on the proposed estimation procedure, we further construct the penalized regression estimators and establish their variable selection consistency and oracle properties. Finite sample properties of our procedures are studied through Monte Carlo simulations. A real data example about the real estate data is used to illustrate our proposed methodology.

Keywords: Partial functional linear regression; Functional principal component analysis; Variable selection; Asymptotic properties (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10182-018-00342-0

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