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    Gaussian process functional regression modelling for batch data.

    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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    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
    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:
    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
    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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