Structured functional additive regression in reproducing kernel Hilbert spaces

Hongxiao Zhu, Fang Yao, Hao Helen Zhang

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Functional additive models provide a flexible yet simple framework for regressions involving functional predictors. The utilization of a data-driven basis in an additive rather than linear structure naturally extends the classical functional linear model. However, the critical issue of selecting non-linear additive components has been less studied. In this work, we propose a new regularization framework for structure estimation in the context of reproducing kernel Hilbert spaces. The approach proposed takes advantage of functional principal components which greatly facilitates implementation and theoretical analysis. The selection and estimation are achieved by penalized least squares using a penalty which encourages the sparse structure of the additive components. Theoretical properties such as the rate of convergence are investigated. The empirical performance is demonstrated through simulation studies and a real data application.

Original languageEnglish (US)
Pages (from-to)581-603
Number of pages23
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume76
Issue number3
DOIs
StatePublished - Jun 2014

Keywords

  • Additive models
  • Component selection
  • Functional data analysis
  • Principal components
  • Reproducing kernel hilbert space
  • Smoothing spline

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Structured functional additive regression in reproducing kernel Hilbert spaces'. Together they form a unique fingerprint.

Cite this