Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1144955 | Journal of the Korean Statistical Society | 2010 | 12 Pages |
Abstract
We propose a family of robust nonparametric estimators for a regression function based on the kernel method. We establish the asymptotic normality of the estimator under the concentration property on small balls probability measure of the functional explanatory variable when the observations exhibit some kind of dependence. This approach can be applied in time series analysis to make prediction and build confidence bands. We illustrate our methodology on the US electricity consumption data.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Mohammed Attouch, Ali Laksaci, Elias Ould Saïd,