PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On Nonparametric Residual Variance Estimation
Elia Liitiäinen, Francesco Corona and Amaury Lendasse
Neural Processing Letters Volume 28, Number 3, pp. 155-167, 2008.

Abstract

In this paper, the problem of residual variance estimation is examined. The problem is analyzed in a general setting which covers non-additive heteroscedastic noise under non-iid sampling. To address the estimation problem, we suggest a method based on nearest neighbor graphs and we discuss its convergence properties under the assumption of a Hölder continuous regression function. The universality of the estimator makes it an ideal tool in problems with only little prior knowledge available.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4805
Deposited By:Amaury Lendasse
Deposited On:24 March 2009