Varying-coefficient functional linear regression

Yichao Wu, Jianqing Fan, Hans Georg Müller

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

Functional linear regression analysis aims to model regression relations which include a functional predictor. The analog of the regression parameter vector or matrix in conventional multivariate or multipleresponse linear regression models is a regression parameter function in one or two arguments. If, in addition, one has scalar predictors, as is often the case in applications to longitudinal studies, the question arises how to incorporate these into a functional regression model. We study a varying-coefficient approach where the scalar covariates are modeled as additional arguments of the regression parameter function. This extension of the functional linear regression model is analogous to the extension of conventional linear regression models to varying-coefficient models and shares its advantages, such as increased flexibility; however, the details of this extension are more challenging in the functional case. Our methodology combines smoothing methods with regularization by truncation at a finite number of functional principal components. A practical version is developed and is shown to perform better than functional linear regression for longitudinal data. We investigate the asymptotic properties of varying-coefficient functional linear regression and establish consistency properties.

Original languageEnglish (US)
Pages (from-to)730-758
Number of pages29
JournalBernoulli
Volume16
Issue number3
DOIs
StatePublished - Aug 2010

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

Keywords

  • Asymptotics
  • Eigenfunctions
  • Functional data analysis
  • Local polynomial smoothing
  • Longitudinal data
  • Varying-coefficient models

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