PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Non-parametric Residual Variance Estimation in Supervised Learning
Elia Liitiäinen, Amaury Lendasse and Francesco Corona
In: 9th InternationalWork-Conference on Artificial Neural Networks Lecture Notes in Computer Science , 4507/2007 . (2007) Springer-Verlag , Berlin Heidelberg , pp. 63-71. ISBN 978-3-540-73006-4

Abstract

The residual variance estimation problem is well-known in statistics and machine learning with many applications for example in the field of nonlinear modelling. In this paper, we show that the problem can be formulated in a general supervised learning context. Emphasis is on two widely used non-parametric techniques known as the Delta test and the Gamma test. Under some regularity assumptions, a novel proof of convergence of the two estimators is formulated and subsequently verified and compared on two meaningful study cases.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3735
Deposited By:Amaury Lendasse
Deposited On:15 February 2008