A 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.
Publié le : 2004-10-14
Classification:
Adaptation,
between class modulus,
confidence intervals,
coverage,
expected length,
linear functionals,
minimax estimation,
modulus of continuity,
white noise model,
62G99,
62F12,
62F35,
62M99
@article{1098883773,
author = {Cai, T. Tony and Low, Mark G.},
title = {An adaptation theory for nonparametric confidence intervals},
journal = {Ann. Statist.},
volume = {32},
number = {1},
year = {2004},
pages = { 1805-1840},
language = {en},
url = {http://dml.mathdoc.fr/item/1098883773}
}
Cai, T. Tony; Low, Mark G. An adaptation theory for nonparametric confidence intervals. Ann. Statist., Tome 32 (2004) no. 1, pp. 1805-1840. http://gdmltest.u-ga.fr/item/1098883773/