Journal of Statistical Theory and Applications

Volume 17, Issue 2, June 2018, Pages 291 - 306

Performance of the Graybill–Deal Estimator via Pitman Closeness Criterion

Authors
Keyu Nie*, Bikas.K. Sinha, A.S. Hedayat
Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago
Received 23 September 2016, Accepted 8 May 2017, Available Online 30 June 2018.
DOI
https://doi.org/10.2991/jsta.2018.17.2.9How to use a DOI?
Keywords
Graybill-Deal Estimator; Pitman Closeness; Meta Analysis
Abstract

Pitman closeness criterion is a coverage probability-based criterion to examine the relative performances of estimators. Usually, the performance of the standard Graybill-Deal estimator of the common mean has been examined with respect to the mean squared error (variance). In this study we examine its performance with respect to the Pitman closeness criterion. Specifically, we compare a p-source based Graybill-Deal estimator against its q-source based competitors for q (< p)-source subsets of p-source data. The key references to this paper are [5] and [7].

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
291 - 306
Publication Date
2018/06/30
ISSN (Online)
2214-1766
ISSN (Print)
1538-7887
DOI
https://doi.org/10.2991/jsta.2018.17.2.9How 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  - Keyu Nie
AU  - Bikas.K. Sinha
AU  - A.S. Hedayat
PY  - 2018
DA  - 2018/06/30
TI  - Performance of the Graybill–Deal Estimator via Pitman Closeness Criterion
JO  - Journal of Statistical Theory and Applications
SP  - 291
EP  - 306
VL  - 17
IS  - 2
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2018.17.2.9
DO  - https://doi.org/10.2991/jsta.2018.17.2.9
ID  - Nie2018
ER  -