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Modern Nonparametric Statistics: Going Beyond Asymptotic Minimax

dc.date.accessioned2019-10-24T14:03:24Z
dc.date.available2019-10-24T14:03:24Z
dc.date.issued2010
dc.identifier.urihttp://publications.mfo.de/handle/mfo/3174
dc.description.abstractDuring 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.
dc.titleModern Nonparametric Statistics: Going Beyond Asymptotic Minimax
dc.identifier.doi10.14760/OWR-2010-16
local.series.idOWR-2010-16
local.subject.msc62
local.sortindex614
local.date-range28 Mar - 03 Apr 2010
local.workshopcode1013
local.workshoptitleModern Nonparametric Statistics: Going Beyond Asymptotic Minimax
local.organizersLucien Birge, Paris; Iain M. Johnstone, Stanford; Vladimir Spokoiny, Berlin
local.report-nameWorkshop Report 2010,16
local.opc-photo-id1013
local.publishers-doi10.4171/OWR/2010/16
local.ems-referenceBirgé Lucien, Johnstone Iain, Spokoiny Vladimir: Modern Nonparametric Statistics: Going Beyond Asymptotic Minimax. Oberwolfach Rep. 7 (2010), 883-939. doi: 10.4171/OWR/2010/16


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