Approximately Unbiased Estimation of Conditional Variance in Heteroscedastic Kernel Ridge Regression

Cawley, Gavin C. ORCID: https://orcid.org/0000-0002-4118-9095, Talbot, Nicola L. C., Foxall, Robert J., Dorling, Stephen R. and Mandic, Danilo P. (2003) Approximately Unbiased Estimation of Conditional Variance in Heteroscedastic Kernel Ridge Regression. In: European Symposium on Artificial Neural Networks, 2003-04-23 - 2003-04-25.

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Abstract

In this paper we extend a form of kernel ridge regression for data characterised by a heteroscedastic noise process (introduced in Foxall et al. [1]) in order to provide approximately unbiased estimates of the conditional variance of the target distribution. This is achieved by the use of the leave-one-out cross-validation estimate of the conditional mean when fitting the model of the conditional variance. The elimination of this bias is demonstrated on synthetic dataset where the true conditional variance is known.

Item Type:	Conference or Workshop Item (Paper)
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Science > School of Environmental Sciences
Depositing User:	Vishal Gautam
Date Deposited:	22 Jul 2011 13:15
Last Modified:	07 Mar 2023 15:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/21975
DOI:

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