An adaptation theory for nonparametric confidence intervals

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dc.creatorCai, T. Tony
dc.creatorLow, Mark G.
dc.date2005-03-29
dc.date.accessioned2026-07-07T08:06:45Z
dc.date.available2026-07-07T08:06:45Z
dc.descriptionA nonparametric adaptation theory is developed for the construction of confidence intervals for linear functionals. A between class modulus of continuity captures the expected length of adaptive confidence intervals. Sharp lower bounds are given for the expected length and an ordered modulus of continuity is used to construct adaptive confidence procedures which are within a constant factor of the lower bounds. In addition, minimax theory over nonconvex parameter spaces is developed.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053604000000049 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0503662
dc.identifierhttp://arxiv.org/abs/math/0503662
dc.identifierAnnals of Statistics 2004, Vol. 32, No. 5, 1805-1840
dc.identifierdoi:10.1214/009053604000000049
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130713
dc.subjectStatistics Theory
dc.subject62G99 (Primary) 62F12, 62F35, 62M99. (Secondary)
dc.titleAn adaptation theory for nonparametric confidence intervals
dc.typetext

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