Translator Disclaimer
October 2004 An adaptation theory for nonparametric confidence intervals
T. Tony Cai, Mark G. Low
Ann. Statist. 32(5): 1805-1840 (October 2004). DOI: 10.1214/009053604000000049

Abstract

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.

Citation

Download Citation

T. Tony Cai. Mark G. Low. "An adaptation theory for nonparametric confidence intervals." Ann. Statist. 32 (5) 1805 - 1840, October 2004. https://doi.org/10.1214/009053604000000049

Information

Published: October 2004
First available in Project Euclid: 27 October 2004

zbMATH: 1056.62060
MathSciNet: MR2102494
Digital Object Identifier: 10.1214/009053604000000049

Subjects:
Primary: 62G99
Secondary: 62F12, 62F35, 62M99

Rights: Copyright © 2004 Institute of Mathematical Statistics

JOURNAL ARTICLE
36 PAGES


SHARE
Vol.32 • No. 5 • October 2004
Back to Top