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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Non-parametric Residual Variance Estimation in Supervised Learning
verfasst von : Elia Liitiäinen, Amaury Lendasse, Francesco Corona
Erschienen in: Computational and Ambient Intelligence
Verlag: Springer Berlin Heidelberg
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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.