Nonlinear estimation in anisotropic multiindex denoising. Sparse case
Teoriâ veroâtnostej i ee primeneniâ, Tome 52 (2007) no. 1, pp. 150-171
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In dimension one, it has long been observed that the minimax rates of convergences in the scale of Besov spaces present essentially two regimes (and a boundary): dense and the sparse zones. In this paper, we consider the problem of denoising a function depending on a multidimensional variable (for instance, an image), with anisotropic constraints of regularity (especially providing a possible disparity of the inhomogeneous aspect in different directions). The case of the dense zone has been investigated in the former paper [G. Kerkyacharian, O. Lepski, and D. Picard, Probab. Theory Related Fields, 121 (2001), pp. 137–170]. Here, our aim is to investigate the case of the sparse region. This case is more delicate in some aspects. For instance, it was an open question to decide whether this sparse case, in the $d$-dimensional context, has to be split into different regions corresponding to different minimax rates. We will see here that the answer is negative: we still observe a sparse region but with a unique minimax behavior, except, as usual, on the boundary. It is worthwhile to notice that our estimation procedure admits the choice of its parameters under which it is adaptive up to logarithmic factor in the “dense case” [G. Kerkyacharian, O. Lepski, and D. Picard, Probab. Theory Related Fields, 121 (2001), pp. 137–170] and minimax adaptive in the “sparse case”. It is also interesting to observe that in the sparse case the embedding properties of the spaces are fundamental.
Keywords:
nonparametric estimation, denoising, anisotropic smoothness, minimax rate of convergence
Mots-clés : anisotropic Besov spaces.
Mots-clés : anisotropic Besov spaces.
@article{TVP_2007_52_1_a8,
author = {G. Kerkyacharian and O. V. Lepskiǐ and D. Picard},
title = {Nonlinear estimation in anisotropic multiindex denoising. {Sparse} case},
journal = {Teori\^a vero\^atnostej i ee primeneni\^a},
pages = {150--171},
publisher = {mathdoc},
volume = {52},
number = {1},
year = {2007},
language = {en},
url = {http://geodesic.mathdoc.fr/item/TVP_2007_52_1_a8/}
}
TY - JOUR AU - G. Kerkyacharian AU - O. V. Lepskiǐ AU - D. Picard TI - Nonlinear estimation in anisotropic multiindex denoising. Sparse case JO - Teoriâ veroâtnostej i ee primeneniâ PY - 2007 SP - 150 EP - 171 VL - 52 IS - 1 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/TVP_2007_52_1_a8/ LA - en ID - TVP_2007_52_1_a8 ER -
%0 Journal Article %A G. Kerkyacharian %A O. V. Lepskiǐ %A D. Picard %T Nonlinear estimation in anisotropic multiindex denoising. Sparse case %J Teoriâ veroâtnostej i ee primeneniâ %D 2007 %P 150-171 %V 52 %N 1 %I mathdoc %U http://geodesic.mathdoc.fr/item/TVP_2007_52_1_a8/ %G en %F TVP_2007_52_1_a8
G. Kerkyacharian; O. V. Lepskiǐ; D. Picard. Nonlinear estimation in anisotropic multiindex denoising. Sparse case. Teoriâ veroâtnostej i ee primeneniâ, Tome 52 (2007) no. 1, pp. 150-171. http://geodesic.mathdoc.fr/item/TVP_2007_52_1_a8/