Bayesian Simultaneous Estimation for Means in k Sample Problems

11/29/2017
by   Ryo Imai, et al.
0

This paper is concerned with the simultaneous estimation of k population means when one suspects that the k means are nearly equal. As an alternative to the preliminary test estimator based on the test statistics for testing hypothesis of equal means, we derive Bayesian and minimax estimators which shrink individual sample means toward a pooled mean estimator given under the hypothesis. Interestingly, it is shown that both the preliminary test estimator and the Bayesian minimax shrinkage estimators are further improved by shrinking the pooled mean estimator. The performance of proposed shrinkage estimators is investigated by simulation.

READ FULL TEXT

page 1

page 2

page 3

page 4

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
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
03/24/2015

Penalty, Shrinkage, and Preliminary Test Estimators under Full Model Hypothesis

This paper considers a multiple regression model and compares, under ful...
research
04/24/2018

Estimation and inference of domain means subject to shape constraints

Population domain means are frequently expected to respect shape or orde...
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
04/07/2021

Equivariant Estimation of Fréchet Means

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