Non-parametric Residual Variance Estimation in Supervised Learning
Elia Liitiäinen, Amaury Lendasse and Francesco Corona
9th InternationalWork-Conference on Artificial Neural Networks
Lecture Notes in Computer Science
, Berlin Heidelberg
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.