Abstract
In This Paper, the Problem of Residual Variance Estimation is Examined. the Problem is Analyzed in a General Setting Which Covers Non-Additive Heteroscedastic Noise under Non-Iid Sampling. to Address the Estimation Problem, We Suggest a Method based on Nearest Neighbor Graphs and We Discuss its Convergence Properties under the Assumption of a Hölder Continuous Regression Function. the Universality of the Estimator Makes It an Ideal Tool in Problems with Only Little Prior Knowledge Available. © 2008 Springer Science business Media, LLC.
Recommended Citation
E. Liitiäinen et al., "On Nonparametric Residual Variance Estimation," Neural Processing Letters, vol. 28, no. 3, pp. 155 - 167, Springer, Dec 2008.
The definitive version is available at https://doi.org/10.1007/s11063-008-9087-8
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Nearest neighbor; Noise variance; Nonparametric; Residual variance estimation
International Standard Serial Number (ISSN)
1573-773X; 1370-4621
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Dec 2008
