Regression function estimation as a partly inverse problem

Abstract : This paper is about nonparametric regression function estimation. Our estimator is a one step projection estimator obtained by least-squares contrast minimization. The specificity of our work is to consider a new model selection procedure including a cutoff for the underlying matrix inversion, and to provide theoretical risk bounds that apply to non compactly supported bases, a case which was specifically excluded of most previous results. Upper and lower bounds for resulting rates are provided.
Type de document :
Pré-publication, Document de travail
MAP5 2018-01. 2018
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https://hal.archives-ouvertes.fr/hal-01690856
Contributeur : Fabienne Comte <>
Soumis le : jeudi 18 octobre 2018 - 15:04:47
Dernière modification le : jeudi 15 novembre 2018 - 01:11:30

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Regression.pdf
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  • HAL Id : hal-01690856, version 4

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Fabienne Comte, Valentine Genon-Catalot. Regression function estimation as a partly inverse problem. MAP5 2018-01. 2018. 〈hal-01690856v4〉

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