Non-Parametric Residual Variance Estimation in Supervised Learning
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. © Springer-Verlag Berlin Heidelberg 2007.
Recommended Citation
E. Liitiäinen et al., "Non-Parametric Residual Variance Estimation in Supervised Learning," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4507 LNCS, pp. 63 - 71, Springer, Jan 2007.
The definitive version is available at https://doi.org/10.1007/978-3-540-73007-1_9
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-354073006-4
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2007
