Polynomials shrinkage estimators of a multivariate normal mean

07/29/2021
by   Abdelkader Benkhaled, et al.
0

In this work, the estimation of the multivariate normal mean by different classes of shrinkage estimators is investigated. The risk associated with the balanced loss function is used to compare two estimators. We start by considering estimators that generalize the James-Stein estimator and show that these estimators dominate the maximum likelihood estimator (MLE), therefore are minimax, when the shrinkage function satisfies some conditions. Then, we treat estimators of polynomial form and prove the increase of the degree of the polynomial allows us to build a better estimator from the one previously constructed.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/07/2023

Non-minimaxity of debiased shrinkage estimators

We consider the estimation of the p-variate normal mean of X∼ N_p(θ,I) u...
research
02/13/2020

Minimaxity and Limits of Risks Ratios of Shrinkage Estimators of a Multivariate Normal Mean in the Bayesian Case

In this article, we consider two forms of shrinkage estimators of the me...
research
07/04/2020

An Empirical Bayes Approach to Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices

The James-Stein estimator is an estimator of the multivariate normal mea...
research
10/26/2019

Ridge-type Linear Shrinkage Estimation of the Matrix Mean of High-dimensional Normal Distribution

The estimation of the mean matrix of the multivariate normal distributio...
research
04/05/2019

On shrinkage estimation for balanced loss functions

The estimation of a multivariate mean θ is considered under natural modi...
research
10/17/2018

Shrinkage estimation of rate statistics

This paper presents a simple shrinkage estimator of rates based on Bayes...
research
04/07/2021

Equivariant Estimation of Fréchet Means

The Fréchet mean generalizes the concept of a mean to a metric space set...

Please sign up or login with your details

Forgot password? Click here to reset