Polynomial spline estimation for partial functional linear regression models

Jianjun Zhou, Zhao Chen, Qingyan Peng

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1107-1129
Number of pages23
JournalComputational Statistics
Volume31
Issue number3
DOIs
StatePublished - Sep 1 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Mathematics

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