Gaussian Process Models for Non Parametric Functional Regression with Functional Responses
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 3428-3445 |
Journal / Publication | Communications in Statistics - Theory and Methods |
Volume | 44 |
Issue number | 16 |
Publication status | Published - 18 Aug 2015 |
Externally published | Yes |
Link(s)
Abstract
Nonparametric functional model with functional responses has been proposed within the functional reproducing kernel Hilbert spaces (fRKHS) framework. Motivated by its superior performance and also its limitations, we propose a Gaussian process model whose posterior mode coincide with the fRKHS estimator. The Bayesian approach has several advantages compared to its predecessor. We also use the predictive process models adapted from the spatial statistics literature to overcome the computational limitations. Modifications of predictive process models are nevertheless critical in our context to obtain valid inferences. The numerical results presented demonstrate the effectiveness of the modifications.
Research Area(s)
- Functional reproducing kernel Hilbert spaces, Gaussian predictive process models, Markov chain Monte Carlo
Citation Format(s)
Gaussian Process Models for Non Parametric Functional Regression with Functional Responses. / Tang, Xingyu; Hong, Zhaoping; Hu, Yuao et al.
In: Communications in Statistics - Theory and Methods, Vol. 44, No. 16, 18.08.2015, p. 3428-3445.
In: Communications in Statistics - Theory and Methods, Vol. 44, No. 16, 18.08.2015, p. 3428-3445.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review