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Gaussian process functional regression modelling for batch data.

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Shi, J. Q. and Wang, B. (2006) Gaussian process functional regression modelling for batch data. Biometrics. Journal of the International Biometric Society, 63 (3). pp. 714-723. ISSN 1541-0420

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Official URL: http://www3.interscience.wiley.com/cgi-bin/fulltex...

Abstract

A Gaussian process functional regression model is proposed for the analysis of batch data. Covariance structure and mean structure are considered simultaneously, with the covariance structure modelled by a Gaussian process regression model and the mean structure modelled by a functional regression model. The model allows the inclusion of covariates in both the covariance structure and the mean structure. It models the nonlinear relationship between a functional output variable and a set of functional and non-functional covariates. Several applications and simulation studies are reported and show that the method provides very good results for curve fitting and prediction.

Item Type:	Article
Additional Information:	The definitive version is available at http://www3.interscience.wiley.com/cgi-bin/fulltext/118538467/PDFSTART
Keywords:	Batch data; B-spline; Functional data analysis; Gaussian process regression model; Gaussian process functional regression model; Multiple-step-ahead forecasting; Non-parametric curve fitting; Hamilton Institute.
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Mathematics and Statistics
Item ID:	1724
Identification Number:	10.1111/j.1541-0420.2007.00758.x
Depositing User:	Hamilton Editor
Date Deposited:	07 Dec 2009 16:46
Journal or Publication Title:	Biometrics. Journal of the International Biometric Society
Publisher:	Wiley-Blackwell Publishing Ltd.
Refereed:	Yes
Related URLs:	Publisher
URI:	https://mural.maynoothuniversity.ie/id/eprint/1724
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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