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Incorporating auxiliary information for improved prediction using combination of kernel machines
Statistical Methodology (2015)
  • Xiang Zhan
  • Debashis Ghosh, university of colorado denver
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.

Keywords
  • Auxiliary information; Combination of kernels; Hybrid predictor; Kernel ridge regression; Mean squared prediction error
Disciplines
Publication Date
2015
Citation Information
Xiang Zhan and Debashis Ghosh. "Incorporating auxiliary information for improved prediction using combination of kernel machines" Statistical Methodology (2015)
Available at: http://works.bepress.com/debashis_ghosh/62/