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

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Date Posted: 27 November 2017

This document has been peer reviewed.