Gaussian process functional regression modeling for batch data.
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.
Shi
J Q
JQ
School of Mathematics and Statistics, University of Newcastle, Newcastle Upon Tyne NE1 7RU, UK. j.q.shi@ncl.ac.uk
Wang
B
B
Murray-Smith
R
R
Titterington
D M
DM
D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D009068:Movement; D016011:Normal Distribution; D010264:Paraplegia / Q000503:physiopathology*; D011187:Posture; D012044:Regression Analysis; D013647:Task Performance and Analysis