| Issue |
ESAIM: PS
Volume 17, 2013
|
|
|---|---|---|
| Page(s) | 472 - 484 | |
| DOI | https://doi.org/10.1051/ps/2012002 | |
| Published online | 03 June 2013 | |
Why minimax is not that pessimistic∗
L2S, SUPELEC, CNRS, University Paris-Sud,
3 rue Joliot-Curie,
91190
Gif-Sur-Yvette,
France
This email address is being protected from spambots. You need JavaScript enabled to view it.
Received:
21
April
2011
Abstract
In nonparametric statistics a classical optimality criterion for estimation procedures is provided by the minimax rate of convergence. However this point of view can be subject to controversy as it requires to look for the worst behavior of an estimation procedure in a given space. The purpose of this paper is to introduce a new criterion based on generic behavior of estimators. We are here interested in the rate of convergence obtained with some classical estimators on almost every, in the sense of prevalence, function in a Besov space. We also show that generic results coincide with minimax ones in these cases.
Mathematics Subject Classification: 62C20 / 28C20 / 46E35
Key words: Minimax theory / maxiset theory / Besov spaces / prevalence / wavelet bases
This work was performed when the author was at LTCI, Telecom ParisTech.
© EDP Sciences, SMAI, 2013
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