Volume 7 Number 4 (Oct. 2017)
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IJAPM 2017 Vol.7(4): 251-258 ISSN: 2010-362X
doi: 10.17706/ijapm.2017.7.4.251-258

Exponential Inequalities in Functional Nonparametrics Regression for Mixing Process

K. Belaide
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.
The goal is to estimate the functional θ when the variable Z is contaminated by measurements errors.

Index Terms—Convolution linear compact operator, kernel estimator, mixing process, non parametric regression.

The author is with Department of Mathematics, Univ. A/Mira Bejaia, Algeria (email: k_tim2002@yahoo.fr).

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.

General Information

ISSN: 2010-362X (Online)
Abbreviated Title: Int. J. Appl. Phys. Math.
Frequency: Quarterly
DOI: 10.17706/IJAPM
Editor-in-Chief: Prof. Haydar Akca 
Abstracting/ Indexing:  Index Copernicus, EI (INSPEC, IET), Chemical Abstracts Services (CAS), Nanowerk Database, Google Scholar, EBSCO, etc.
E-mail: ijapm@iap.org
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