Polynomial spline estimation for partial functional linear regression models

Jianjun Zhou (), Zhao Chen and Qingyan Peng
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Jianjun Zhou: Yunnan University
Zhao Chen: The Pennsylvania State University
Qingyan Peng: Yunnan University

Computational Statistics, 2016, vol. 31, issue 3, No 14, 1107-1129

Abstract: Abstract Because of its orthogonality, interpretability and best representation, functional principal component analysis approach has been extensively used to estimate the slope function in the functional linear model. However, as a very popular smooth technique in nonparametric/semiparametric regression, polynomial spline method has received little attention in the functional data case. In this paper, we propose the polynomial spline method to estimate a partial functional linear model. Some asymptotic results are established, including asymptotic normality for the parameter vector and the global rate of convergence for the slope function. Finally, we evaluate the performance of our estimation method by some simulation studies.

Keywords: Functional data analysis; Polynomial spline; Asymptotic normality; Rates of convergence (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s00180-015-0636-0

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