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 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.
Original language | English |
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Pages (from-to) | 714-723 |
Number of pages | 9 |
Journal | Biometrics (Journal of the International Biometric Society) |
Volume | 63 |
Issue number | 3 |
DOIs | |
Publication status | Published - 27 Feb 2007 |
Keywords
- Batch data • B-spline • Functional data analysis • Gaussian process functional regression model • Gaussian process regression model • Multiple-step-ahead forecasting • Nonparametric curve fitting