PARTIALLY LINEAR ADDITIVE FUNCTIONAL REGRESSION
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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Detail(s)
Original language | English |
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Pages (from-to) | 2199-2216 |
Journal / Publication | Statistica Sinica |
Volume | 32 |
Issue number | 4 |
Publication status | Published - Oct 2022 |
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DOI | DOI |
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Attachment(s) | Documents
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85159209335&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(0363b274-aadb-41f2-9e89-0ad5fa80d59d).html |
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.
In: Statistica Sinica, Vol. 32, No. 4, 10.2022, p. 2199-2216.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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