Oberwolfach Reports
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Volume 7, Issue 1, 2010, pp. 883–939
DOI: 10.4171/OWR/2010/16
Modern Nonparametric Statistics: Going Beyond Asymptotic Minimax
Organized by: Lucien Birgé (1), Iain M. Johnstone (2) and Vladimir Spokoiny (3)
(1) UMR 7599 "Probabilités et modèles aléatoires", Lab, Université Paris VI, 4, place Jussieu, boîte 188, 75252, PARIS CEDEX 05, FRANCE(2) Department of Statistics, Stanford University, Sequoia Hall, 390 Serra Mall, CA 94305-4065, STANFORD, UNITED STATES
(3) Weierstrass Institut für, Angewandte Analysis und Stochastik, Mohrenstr 39, D-10117, BERLIN, GERMANY
During the years 1975-1990 a major emphasis in nonparametric estimation was put on computing the asymptotic minimax risk for many classes of functions. Modern statistical practice indicates some serious limitations of the asymptotic minimax approach and calls for some new ideas and methods which can cope with the numerous challenges brought to statisticians by modern sets of data.
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