Incorporating auxiliary information for improved prediction using combination of kernel machines

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.