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Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression

by   Meimei Liu, et al.
Duke University
Purdue University

In this paper, we propose a random projection approach to estimate variance in kernel ridge regression. Our approach leads to a consistent estimator of the true variance, while being computationally more efficient. Our variance estimator is optimal for a large family of kernels, including cubic splines and Gaussian kernels. Simulation analysis is conducted to support our theory.


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