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

03/19/2020
by   Yuzo Maruyama, et al.
0

We study admissibility of a subclass of generalized Bayes estimators of a multivariate normal vector when the variance is unknown, under scaled quadratic loss. Minimaxity is also established for certain of these estimators.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/13/2021

Generalized Bayes Estimators with Closed forms for the Normal Mean and Covariance Matrices

In the estimation of the mean matrix in a multivariate normal distributi...
research
02/24/2021

On admissible estimation of a mean vector when the scale is unknown

We consider admissibility of generalized Bayes estimators of the mean of...
research
06/22/2022

Ensemble minimaxity of James-Stein estimators

This article discusses estimation of a multivariate normal mean based on...
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
04/11/2020

Robust Generalised Quadratic Discriminant Analysis

Quadratic discriminant analysis (QDA) is a widely used statistical tool ...
research
06/05/2018

Pathwise Derivatives for Multivariate Distributions

We exploit the link between the transport equation and derivatives of ex...
research
06/07/2018

Inference for a constrained parameter in presence of an uncertain constraint

We describe a hierarchical Bayesian approach for inference about a param...

Please sign up or login with your details

Forgot password? Click here to reset