Non-minimaxity of debiased shrinkage estimators

06/07/2023
by   Yuzo Maruyama, et al.
0

We consider the estimation of the p-variate normal mean of X∼ N_p(θ,I) under the quadratic loss function. We investigate the decision theoretic properties of debiased shrinkage estimator, the estimator which shrinks towards the origin for smaller x^2 and which is exactly equal to the unbiased estimator X for larger x^2. Such debiased shrinkage estimator seems superior to the unbiased estimator X, which implies minimaxity. However we show that it is not minimax under mild conditions.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/29/2021

Polynomials shrinkage estimators of a multivariate normal mean

In this work, the estimation of the multivariate normal mean by differen...
research
10/17/2018

Shrinkage estimation of rate statistics

This paper presents a simple shrinkage estimator of rates based on Bayes...
research
09/18/2020

The Stein Effect for Frechet Means

The Frechet mean is a useful description of location for a probability d...
research
04/03/2020

Relaxing the Gaussian assumption in Shrinkage and SURE in high dimension

Shrinkage estimation is a fundamental tool of modern statistics, pioneer...
research
11/29/2017

Bayesian Simultaneous Estimation for Means in k Sample Problems

This paper is concerned with the simultaneous estimation of k population...
research
11/11/2022

Tractable Evaluation of Stein's Unbiased Risk Estimator with Convex Regularizers

Stein's unbiased risk estimate (SURE) gives an unbiased estimate of the ...
research
10/31/2022

Shrinkage Methods for Treatment Choice

This study examines the problem of determining whether to treat individu...

Please sign up or login with your details

Forgot password? Click here to reset