A functional single-index model

Fei Jiang, Seungchul Baek, Jiguo Cao, Yanyuan Ma

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We propose a semiparametric functional single-index model for studying the relationship between a univariate response and multiple functional covariates. The parametric part of the model integrates a functional linear regression model and a sufficient dimension-reduction structure. The nonparametric part of the model allows the response-index dependence or the link function to be unspecified. The B-spline method is used to approximate the coefficient function, which leads to a dimension-folding-type model. A new kernel regression method is developed to handle the dimension-folding model, allowing us to estimate the index vector and the B-spline coefficients efficiently. We also establish the asymptotic properties and semiparametric optimality for the estimators.

Original languageEnglish (US)
Pages (from-to)303-324
Number of pages22
JournalStatistica Sinica
Volume30
Issue number1
DOIs
StatePublished - Jan 1 2020

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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