doi: 10.17706/ijapm.2017.7.4.251-258
Exponential Inequalities in Functional Nonparametrics Regression for Mixing Process
K. Belaide
- Department of Mathematics, Univ. A/Mira Bejaia, Algeria
Abstract
This paper establishes exponential inequalities for the probability of the distance between kernel estimator and its means in nonparametric regression problem with mixing variables. We consider an operator equation taking the following form Y=Aθ(Z)+ε, where A is a compact operator.<br /> The goal is to estimate the functional θ when the variable Z is contaminated by measurements errors.
Keywords
- Convolution linear compact operator
- kernel estimator
- mixing process
- non parametric regression
How to Cite
K. Belaide, "Exponential Inequalities in Functional Nonparametrics Regression for Mixing Process," International Journal of Applied Physics and Mathematics, vol. 7, no. 4, pp. 251-258, 2017. https://doi.org/ 10.17706/ijapm.2017.7.4.251-258
Copyright & License
Copyright © 2017 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).