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- W2009590904 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." @default.
- W2009590904 created "2016-06-24" @default.
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- W2009590904 date "2015-01-01" @default.
- W2009590904 modified "2023-09-24" @default.
- W2009590904 title "Incorporating auxiliary information for improved prediction using combination of kernel machines" @default.
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- W2009590904 doi "https://doi.org/10.1016/j.stamet.2014.08.001" @default.
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