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

Christophe Chesneau | Zendy; Maher Kachour | Zendy

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

Author(s) -

Christophe Chesneau,

Maher Kachour

Publication year - 2015

Publication title -

advances in statistics

Language(s) - English

Resource type - Journals

eISSN - 2356-6892

pISSN - 2314-8314

DOI - 10.1155/2015/695904

Subject(s) - convolution (computer science) , estimator , function (biology) , mathematics , computer science , regression , estimation , regression analysis , statistics , algorithm , artificial intelligence , engineering , evolutionary biology , artificial neural network , biology , systems engineering

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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