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arXiv · 1809.06019

Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression

Meimei Liu·Jean Honorio·Guang Cheng
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Abstract

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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BibTeXRIS

Meimei Liu, Jean Honorio, Guang Cheng. 2018-09-17. Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression. https://arxiv.org/abs/1809.06019

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