Approximately Unbiased Estimation of Conditional Variance in Heteroscedastic Kernel Ridge Regression

Gavin C. Cawley, Nicola L. C. Talbot, Robert J. Foxall, Stephen R. Dorling, Danilo P. Mandic

Research output: Contribution to conferencePaper


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.
Original languageEnglish
Number of pages6
Publication statusPublished - Apr 2003
EventEuropean Symposium on Artificial Neural Networks - Bruges, Belgium
Duration: 23 Apr 200325 Apr 2003


ConferenceEuropean Symposium on Artificial Neural Networks
Abbreviated titleESANN-2003

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