Estimation of a Normal Covariance Matrix with Incomplete Data under Stein's Loss

Y. Konno

Journal of Multivariate Analysis, 1995, vol. 52, issue 2, 308-324

Abstract: Suppose that we have (n - a) independent observations from Np(0, [Sigma]) and that, in addition, we have a independent observations available on the last (p - c) coordinates. Assuming that both observations are independent, we consider the problem of estimating [Sigma] under the Stein's loss function, and show that some estimators invariant under the permutation of the last (p - c) coordinates as well as under those of the first c coordinates are better than the minimax estimators of Eaten. The estimators considered outperform the maximum likelihood estimator (MLE) under the Stein's loss function as well. The method involved here is computation of an unbiased estimate of the risk of an invariant estimator considered in this article. In addition we discuss its application to the problem of estimating a covariance matrix in a GMANOVA model since the estimation problem of the covariance matrix with extra data can be regarded as its canonical form.

Date: 1995
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047-259X(85)71016-0
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:52:y:1995:i:2:p:308-324

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

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 ().