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April 2005 Minimax estimation of the noise level and of the deconvolution density in a semiparametric convolution model
Cristina Butucea, Catherine Matias
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Bernoulli 11(2): 309-340 (April 2005). DOI: 10.3150/bj/1116340297


We consider a semiparametric convolution model where the noise has known Fourier transform which decays asymptotically as an exponential with unknown scale parameter; the deconvolution density is less smooth than the noise in the sense that the tails of the Fourier transform decay more slowly, ensuring the identifiability of the model. We construct a consistent estimation procedure for the noise level and prove that its rate is optimal in the minimax sense. Two convergence rates are distinguished according to different smoothness properties for the unknown density. If the tail of its Fourier transform does not decay faster than exponentially, the asymptotic optimal rate and exact constant are evaluated, while if it does not decay faster than polynomially, this rate is evaluated up to a constant. Moreover, we construct a consistent estimator of the unknown density, by using a plug-in method in the classical kernel estimation procedure. We establish that the rates of estimation of the deconvolution density are slower than in the case of an entirely known noise distribution. In fact, nonparametric rates of convergence are equal to the rate of estimation of the noise level, and we prove that these rates are minimax. In a few specific cases the plug-in method converges at even slower rates.


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Cristina Butucea. Catherine Matias. "Minimax estimation of the noise level and of the deconvolution density in a semiparametric convolution model." Bernoulli 11 (2) 309 - 340, April 2005.


Published: April 2005
First available in Project Euclid: 17 May 2005

zbMATH: 1063.62044
MathSciNet: MR2132729
Digital Object Identifier: 10.3150/bj/1116340297

Keywords: analytic densities , Deconvolution , L_2 risk , minimax estimation , noise level , pointwise risk , Semiparametric model , Sobolev classes , supersmooth densities

Rights: Copyright © 2005 Bernoulli Society for Mathematical Statistics and Probability


Vol.11 • No. 2 • April 2005
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