Analysis of regularized least squares for functional linear regression model

Hongzhi Tong, Kwok Po NG*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

In this paper, we study and analyze the regularized least squares for functional linear regression model. The approach is to use the reproducing kernel Hilbert space framework and the integral operators. We show with a more general and realistic assumption on the reproducing kernel and input data statistics that the rate of excess prediction risk by the regularized least squares is minimax optimal.

Original languageEnglish
Pages (from-to)85-94
Number of pages10
JournalJournal of Complexity
Volume49
DOIs
Publication statusPublished - Dec 2018

Scopus Subject Areas

  • Algebra and Number Theory
  • Statistics and Probability
  • Numerical Analysis
  • Mathematics(all)
  • Control and Optimization
  • Applied Mathematics

User-Defined Keywords

  • Functional linear regression
  • Learning rate
  • Regularized least squares
  • Reproducing kernel Hilbert space

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