Statistics Papers
Document Type
Journal Article
Date of this Version
1996
Publication Source
The Annals of Statistics
Volume
24
Issue
6
Start Page
2524
Last Page
2535
DOI
10.1214/aos/1032181166
Abstract
A general constrained minimum risk inequality is derived. Given two densities fθ and f0 we find a lower bound for the risk at the point θ given an upper bound for the risk at the point 0. The inequality sheds new light on superefficient estimators in the normal location problem and also on an adaptive estimation problem arising in nonparametric functional estimation.
Keywords
adaptive estimation, superefficient estimators, nonparametric functional estimation, minimum risk inequalities, white noise model, density estimation, nonparametric regression
Recommended Citation
Brown, L. D., & Low, M. G. (1996). A Constrained Risk Inequality With Applications to Nonparametric Functional Estimation. The Annals of Statistics, 24 (6), 2524-2535. http://dx.doi.org/10.1214/aos/1032181166
Date Posted: 27 November 2017
This document has been peer reviewed.