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Estimation of the Derivatives of a Function in a Convolution Regression Model with Random Design


Affiliations
1 Laboratoire de Mathematiques Nicolas Oresme, Universite de Caen, BP 5186, 14032 Caen Cedex, France
2 Ecole Superieure de Commerce IDRAC, 47 rue Sergent Michel Berthet, CP 607, 69258 Lyon Cedex 09, France
 

A convolution regression model with random design is considered. We investigate the estimation of the derivatives of an unknown function, element of the convolution product. We introduce new estimators based on wavelet methods and provide theoretical guarantees on their good performances.
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  • Estimation of the Derivatives of a Function in a Convolution Regression Model with Random Design

Abstract Views: 106  |  PDF Views: 6

Authors

Christophe Chesneau
Laboratoire de Mathematiques Nicolas Oresme, Universite de Caen, BP 5186, 14032 Caen Cedex, France
Maher Kachour
Ecole Superieure de Commerce IDRAC, 47 rue Sergent Michel Berthet, CP 607, 69258 Lyon Cedex 09, France

Abstract


A convolution regression model with random design is considered. We investigate the estimation of the derivatives of an unknown function, element of the convolution product. We introduce new estimators based on wavelet methods and provide theoretical guarantees on their good performances.