In this paper, we consider the problem of estimating the covariance matrix and the generalized variance when the observations follow a nonsingular multivariate normal distribution with unknown mean. A new method is presented to obtain a truncated estimator that utilizes the information available in the sample mean matrix and dominates the James-Stein minimax estimator. Several scale equivariant minimax estimators are also given. This method is then applied to obtain new truncated and improved estimators of the generalized variance; it also provides a new proof to the results of Shorro k and Zidek (1976)and Sinha (1976).
内容記述
Former Version: 1999-CF-52
本文フィルはリンク先を参照のこと
雑誌名
Discussion paper series. CIRJE-F
巻
2002-CF-162
発行年
2002-07
書誌レコードID
AA11450569
フォーマット
application/pdf
日本十進分類法
330
Mathmatical Subject Classification
62F11
62J12
62C15
62C20
出版者
日本経済国際共同センター
出版者別名
Center for International Research on the Japanese Economy
Estimating the Covariance Matrix : A New Approach
