James–Stein type estimators of variances

Tiejun Tong, Homin Jang and Yuedong Wang

Journal of Multivariate Analysis, 2012, vol. 107, issue C, 232-243

Abstract: In this paper we propose James–Stein type estimators for variances raised to a fixed power by shrinking individual variance estimators towards the arithmetic mean. We derive and estimate the optimal choices of shrinkage parameters under both the squared and the Stein loss functions. Asymptotic properties are investigated under two schemes when either the number of degrees of freedom of each individual estimate or the number of individuals approaches to infinity. Simulation studies indicate that the performance of various shrinkage estimators depends on the loss function, and the proposed estimator outperforms existing methods under the squared loss function.

Keywords: Inadmissibility; Shrinkage estimation; Shrinkage parameter; Squared loss function; Stein loss function; Variance estimation (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X12000206
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:107:y:2012:i:c:p:232-243

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2012.01.019

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().