title: Gaussian process functional regression modeling for batch data.
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.
authors.affiliation: School of Mathematics and Statistics, University of Newcastle, Newcastle Upon Tyne NE1 7RU, UK. j.q.shi@ncl.ac.uk
authors.forename: J Q
authors.initials: JQ
authors.lastname: Shi
authors.forename: B
authors.initials: B
authors.lastname: Wang
authors.forename: R
authors.initials: R
authors.lastname: Murray-Smith
authors.forename: D M
authors.initials: DM
authors.lastname: Titterington
mesh_terms: 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