期刊
STATISTICAL METHODOLOGY
卷 22, 期 -, 页码 47-57出版社
ELSEVIER
DOI: 10.1016/j.stamet.2014.08.001
关键词
Auxiliary information; Combination of kernels; Hybrid predictor; Kernel ridge regression; Mean squared prediction error
资金
- NIH [R01 CA 129102]
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 usingX. 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. (C) 2014 Elsevier B.V. All rights reserved.
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