Incorporating auxiliary information for improved prediction using combination of kernel machines

Xiang Zhan, Debashis Ghosh

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)47-57
Number of pages11
JournalStatistical Methodology
StatePublished - Jan 1 2015

All Science Journal Classification (ASJC) codes

  • Statistics and Probability


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