PARTIALLY LINEAR ADDITIVE FUNCTIONAL REGRESSION

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Scopus Citations
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Author(s)

  • Xiaohui Liu
  • Wenqi Lu
  • Heng Lian
  • Yuzi Liu
  • Zhongyi Zhu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)2199-2216
Journal / PublicationStatistica Sinica
Volume32
Issue number4
Publication statusPublished - Oct 2022

Link(s)

Abstract

We consider a novel partially linear additive functional regression model in which both a functional predictor and some scalar predictors appear. The functional part has a semiparametric continuously additive form, while the scalar predictors appear in the linear part. The functional part has the optimal convergence rate, and the asymptotic normality of the nonfunctional part is also shown. Simulations and an empirical analysis of a Covid-19 data set demonstrate the performance of the proposed estimator.

Research Area(s)

  • Convergence rate, functional data, penalization, RKHS, EFFICIENT ESTIMATION, MODELS, CONVERGENCE, SELECTION, SINGLE

Citation Format(s)

PARTIALLY LINEAR ADDITIVE FUNCTIONAL REGRESSION. / Liu, Xiaohui; Lu, Wenqi; Lian, Heng et al.
In: Statistica Sinica, Vol. 32, No. 4, 10.2022, p. 2199-2216.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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