Shi, J.Q., Wang, B., Murray-Smith, R. and Titterington, D.M. (2007) Gaussian Process Functional Regression Modelling for Batch Data. Biometrics (Journal of the International Biometric Society), 63 ( 3). pp. 714-723. ISSN 1541-0420Full text not available from this repository.
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 modeled by a Gaussian process regression model and the mean structure modeled 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 nonfunctional covariates. Several applications and simulation studies are reported and show that the method provides very good results for curve fitting and prediction.
|Keywords:||Batch data • B-spline • Functional data analysis • Gaussian process functional regression model • Gaussian process regression model • Multiple-step-ahead forecasting • Nonparametric curve fitting|
|Institution:||The University of York|
|Academic Units:||The University of York > Mathematics (York)|
|Depositing User:||York RAE Import|
|Date Deposited:||13 Aug 2009 14:26|
|Last Modified:||13 Aug 2009 14:26|