On Nonparametric Residual Variance Estimation

Authors:
Elia Liitiäinen;Francesco Corona;Amaury Lendasse
Affiliations:
Department of Computer Science and Engineering, Helsinki University of Technology, Espoo, Finland 2015;Department of Computer Science and Engineering, Helsinki University of Technology, Espoo, Finland 2015;Department of Computer Science and Engineering, Helsinki University of Technology, Espoo, Finland 2015
Venue:
Neural Processing Letters
Year:
2008

Citing 8
Cited 5

The geometry of fractal sets

The geometry of fractal sets
Real and complex analysis, 3rd ed.

Real and complex analysis, 3rd ed.
Local polynomial variance-function estimation

Technometrics
Variance estimation for high-dimensional regression models

Journal of Multivariate Analysis
The differogram: Non-parametric noise variance estimation and its use for model selection

Neurocomputing
Non-parametric residual variance estimation in supervised learning

IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
LS-SVM hyperparameter selection with a nonparametric noise estimator

ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Rates of convergence of nearest neighbor estimation under arbitrary sampling

IEEE Transactions on Information Theory

RCGA-S/RCGA-SP Methods to Minimize the Delta Test for Regression Tasks

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Residual variance estimation in machine learning

Neurocomputing
Residual variance estimation using a nearest neighbor statistic

Journal of Multivariate Analysis
Approximate k-NN delta test minimization method using genetic algorithms: Application to time series

Neurocomputing
A boundary corrected expansion of the moments of nearest neighbor distributions

Random Structures & Algorithms

Quantified Score

Hi-index	0.00

Visualization

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.