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Title: Gaussian process methods for nonparametric functional regression with mixed predictors
Authors: Wang, Bo
Xu, Aiping
First Published: 26-Jul-2018
Publisher: Elsevier
Citation: Computational Statistics and Data Analysis, Special Issue: High-dimensional and functional data analysis, 2019, 131, pp. 80-90
Abstract: Gaussian process methods are proposed for nonparametric functional regression for both scalar and functional responses with mixed multidimensional functional and scalar predictors. The proposed models allow the response variables to depend on the entire trajectories of the functional predictors. They inherit the desirable properties of Gaussian process regression, and can naturally accommodate both scalar and functional variables as the predictors, as well as easy to obtain and express uncertainty in predictions. The numerical experiments show that the proposed methods significantly outperform the competing models, and their usefulness is also demonstrated by the application to two real datasets.
DOI Link: 10.1016/j.csda.2018.07.009
ISSN: 0167-9473
eISSN: 1872-7352
Embargo on file until: 26-Jul-2019
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2018. Deposited with reference to the publisher’s open access archiving policy. (
Description: The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.
Appears in Collections:Published Articles, Dept. of Mathematics

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