Please use this identifier to cite or link to this item:
|Title:||Mixed-effects Gaussian process functional regression models with application to dose-response curve prediction.|
|Citation:||STAT MED, 2012|
|Abstract:||We propose a new semiparametric model for functional regression analysis, combining a parametric mixed-effects model with a nonparametric Gaussian process regression model, namely a mixed-effects Gaussian process functional regression model. The parametric component can provide explanatory information between the response and the covariates, whereas the nonparametric component can add nonlinearity. We can model the mean and covariance structures simultaneously, combining the information borrowed from other subjects with the information collected from each individual subject. We apply the model to dose-response curves that describe changes in the responses of subjects for differing levels of the dose of a drug or agent and have a wide application in many areas. We illustrate the method for the management of renal anaemia. An individual dose-response curve is improved when more information is included by this mechanism from the subject/patient over time, enabling a patient-specific treatment regime. Copyright © 2012 John Wiley & Sons, Ltd.|
|Appears in Collections:||Published Articles, Dept. of Mathematics|
Files in This Item:
There are no files associated with this item.
Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.