Date of this Version
The Annals of Statistics
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.
adaptive estimation, superefficient estimators, nonparametric functional estimation, minimum risk inequalities, white noise model, density estimation, nonparametric regression
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.