Sharp optimality in density deconvolution with dominating bias. II
Teoriâ veroâtnostej i ee primeneniâ, Tome 52 (2007) no. 2, pp. 336-349
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We consider estimation of the common probability density $f$ of iid random variables $X_i$ that are observed with an additive iid noise. We assume that the unknown density $f$ belongs to a class $\mathcal{A}$ of densities whose characteristic function is described by the exponent $\exp(-\alpha |u|^r)$ as $|u|\to\infty$, where $\alpha>0$, $r>0$. The noise density is assumed known and such that its characteristic function decays as $\exp(-\beta|u|^s)$, as $|u|\to\infty$, where $\beta>0$, $s>0$. Assuming that $r, we suggest a kernel-type estimator, whose variance turns out to be asymptotically negligible with respect to its squared bias under both pointwise and $\mathbb{L}_2$ risks. For $r we construct a sharp adaptive estimator of $f$.
Keywords:
nonparametric density estimation, infinitely differentiable functions, exact constants in nonparametric smoothing, minimax risk, adaptive curve estimation.
Mots-clés : deconvolution
Mots-clés : deconvolution
@article{TVP_2007_52_2_a4,
author = {C. Butucea and A. Tsybakov},
title = {Sharp optimality in density deconvolution with dominating {bias.~II}},
journal = {Teori\^a vero\^atnostej i ee primeneni\^a},
pages = {336--349},
year = {2007},
volume = {52},
number = {2},
language = {en},
url = {http://geodesic.mathdoc.fr/item/TVP_2007_52_2_a4/}
}
C. Butucea; A. Tsybakov. Sharp optimality in density deconvolution with dominating bias. II. Teoriâ veroâtnostej i ee primeneniâ, Tome 52 (2007) no. 2, pp. 336-349. http://geodesic.mathdoc.fr/item/TVP_2007_52_2_a4/
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