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Article Dans Une Revue Canadian Journal of Statistics Année : 2013

Fast nonparametric estimation for convolutions of densities

Résumé

The present paper is concerned with the problem of estimating the convolution of densities. We propose an adaptive estimator based on kernel methods, Fourier analysis and the Lepski method. We study its $\mathbb{L}_2$-risk properties. Fast and new rates of convergence are determined for a wide class of unknown functions. Numerical illustrations, on both simulated and real data, are provided to assess the performances of our estimator.
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Dates et versions

hal-00798766 , version 1 (10-03-2013)
hal-00798766 , version 2 (20-03-2013)

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Citer

Christophe Chesneau, Fabienne Comte, Fabien Navarro. Fast nonparametric estimation for convolutions of densities. Canadian Journal of Statistics, 2013, 41 (4), pp.617-636. ⟨10.1002/cjs.11191⟩. ⟨hal-00798766v2⟩
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