Ensemble minimaxity of James-Stein estimators

06/22/2022
by   Yuzo Maruyama, et al.
0

This article discusses estimation of a multivariate normal mean based on heteroscedastic observations. Under heteroscedasticity, estimators shrinking more on the coordinates with larger variances, seem desirable. Although they are not necessarily minimax in the ordinary sense, we show that such James-Stein type estimators can be ensemble minimax, minimax with respect to the ensemble risk, related to empirical Bayes perspective of Efron and Morris.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/19/2020

Admissible estimators of a multivariate normal mean vector when the scale is unknown

We study admissibility of a subclass of generalized Bayes estimators of ...
research
05/28/2018

On estimation of nonsmooth functionals of sparse normal means

We study the problem of estimation of the value N_gamma(θ) = sum(i=1)^d ...
research
11/30/2017

Bayes Minimax Competitors of Preliminary Test Estimators in k Sample Problems

In this paper, we consider the estimation of a mean vector of a multivar...
research
05/06/2022

Efficient Minimax Optimal Estimators For Multivariate Convex Regression

We study the computational aspects of the task of multivariate convex re...
research
12/10/2020

Leveraging vague prior information in general models via iteratively constructed Gamma-minimax estimators

Gamma-minimax estimation is an approach to incorporate prior information...
research
07/05/2023

Empirical Bayes via ERM and Rademacher complexities: the Poisson model

We consider the problem of empirical Bayes estimation for (multivariate)...
research
12/06/2021

Minimax properties of Dirichlet kernel density estimators

This paper is concerned with the asymptotic behavior in β-Hölder spaces ...

Please sign up or login with your details

Forgot password? Click here to reset