Journal of Statistical Theory and Applications

Volume 17, Issue 2, June 2018, Pages 324 - 339

A class of Bivariate SURE estimators in heteroscedastic hierarchical normal models

Authors
S.K. Ghoreishiatty_ghoreishi@yahoo.com
Department of Statistics, Faculty of Sciences, University of Qom, Qom, Iran
Received 11 January 2017, Accepted 5 June 2017, Available Online 30 June 2018.
DOI
https://doi.org/10.2991/jsta.2018.17.2.11How to use a DOI?
Keywords
Asymptotic univariate shrinkage estimators; Heteroscedasticity; Hierarchical models; Multivariate shrinkage estimator; Stein’s unbiased risk estimators
Abstract

In this paper, we first propose a class of bivariate shrinkage estimators based on Steins unbiased estimate of risk (SURE). Then, we study the effect of correlation coefficients on their performance. Moreover, under some mild assumptions on the model correlations, we set up the optimal asymptotic properties of our estimates when the number of vector means to be estimated grows. Furthermore, we carry out a simulation study to compare how various bivariate competing shrinkage estimators perform and analyze a real data set.

Copyright
Copyright © 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

Journal
Journal of Statistical Theory and Applications
Volume-Issue
17 - 2
Pages
324 - 339
Publication Date
2018/06/30
ISSN (Online)
2214-1766
ISSN (Print)
1538-7887
DOI
https://doi.org/10.2991/jsta.2018.17.2.11How to use a DOI?
Copyright
Copyright © 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - S.K. Ghoreishi
PY  - 2018
DA  - 2018/06/30
TI  - A class of Bivariate SURE estimators in heteroscedastic hierarchical normal models
JO  - Journal of Statistical Theory and Applications
SP  - 324
EP  - 339
VL  - 17
IS  - 2
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2018.17.2.11
DO  - https://doi.org/10.2991/jsta.2018.17.2.11
ID  - Ghoreishi2018
ER  -