Gaussian process methods for nonparametric functional regression with mixed predictors

Bo Wang and Aiping Xu

Computational Statistics & Data Analysis, 2019, vol. 131, issue C, 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.

Keywords: Functional regression; Functional principal component analysis; Gaussian process regression; Nonparametric methods; Semi-metric (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:131:y:2019:i:c:p:80-90

DOI: 10.1016/j.csda.2018.07.009

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