# Why minimax is not that pessimistic

ESAIM: Probability and Statistics (2013)

- Volume: 17, page 472-484
- ISSN: 1292-8100

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topFraysse, Aurelia. "Why minimax is not that pessimistic." ESAIM: Probability and Statistics 17 (2013): 472-484. <http://eudml.org/doc/273625>.

@article{Fraysse2013,

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.},

author = {Fraysse, Aurelia},

journal = {ESAIM: Probability and Statistics},

keywords = {minimax theory; maxiset theory; Besov spaces; prevalence; wavelet bases},

language = {eng},

pages = {472-484},

publisher = {EDP-Sciences},

title = {Why minimax is not that pessimistic},

url = {http://eudml.org/doc/273625},

volume = {17},

year = {2013},

}

TY - JOUR

AU - Fraysse, Aurelia

TI - Why minimax is not that pessimistic

JO - ESAIM: Probability and Statistics

PY - 2013

PB - EDP-Sciences

VL - 17

SP - 472

EP - 484

AB - 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.

LA - eng

KW - minimax theory; maxiset theory; Besov spaces; prevalence; wavelet bases

UR - http://eudml.org/doc/273625

ER -

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