Cover: Estimation of a Normal Covariance Matrix.

Estimation of a Normal Covariance Matrix.

Published 1975

by S. James Press

Purchase Print Copy

 Format Price
Add to Cart Paperback8 pages $20.00

Considers the problem of estimating the covariance matrix of a normal distribution. In very large samples the maximum likelihood estimator (MLE) is of course "best" in many respects. In small or moderate samples, however, it is not surprising to find challenges to the MLE's superiority. It is shown that there are admissible estimators which improve upon the MLE, relative to a quadratic loss function, uniformly for all values of the covariance matrix and all sample sizes. 8 pp. Ref.

This report is part of the RAND paper series. The paper was a product of RAND from 1948 to 2003 that captured speeches, memorials, and derivative research, usually prepared on authors' own time and meant to be the scholarly or scientific contribution of individual authors to their professional fields. Papers were less formal than reports and did not require rigorous peer review.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.