Non-parametric Residual Variance Estimation in Supervised Learning

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.

• Type: Conference
• Year: 2007
• Where: IWANN
• Authors: Elia Liitiäinen, Amaury Lendasse, Francesco Corona
• Keywords: IWANN 2007, Meaningful Study Cases, Supervised Learning Context, Variance Estimation Problem