title: Incorporating auxiliary information for improved prediction using combination of kernel machines.
abstract: With evolving genomic technologies, it is possible to get different measures of the same underlying biological phenomenon using different technologies. The goal of this paper is to build a prediction model for an outcome variable Y from covariates X. Besides X, we have surrogate covariates W which are related to X. We want to utilize the information in W to boost the prediction for Y using X. In this paper, we propose a kernel machine-based method to improve prediction of Y by X by incorporating auxiliary information W. By combining single kernel machines, we also propose a hybrid kernel machine predictor, which can yield a smaller prediction error than its constituents. The prediction error of our kernel machine predictors is evaluated using simulations. We also apply our method to a lung cancer dataset and an Alzheimer's disease dataset.
authors.affiliation: Department of Statistics, Pennsylvania State University, University Park, PA 16802, U.S.A.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhan
authors.affiliation: Department of Statistics, Pennsylvania State University, University Park, PA 16802, U.S.A. ; Department of Public Health Sciences, Pennsylvania State University, University Park, PA 16802, U.S.A.
authors.forename: Debashis
authors.initials: D
authors.lastname: Ghosh
keywords: Auxiliary information; Combination of kernels; Hybrid predictor; Kernel ridge regression; Mean squared prediction error