Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression
Statistics Theory
2018-09-18 v1 Machine Learning
Machine Learning
Statistics Theory
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.
Cite
@article{arxiv.1809.06019,
title = {Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression},
author = {Meimei Liu and Jean Honorio and Guang Cheng},
journal= {arXiv preprint arXiv:1809.06019},
year = {2018}
}
Comments
To Appear in 2018 Allerton