James-Stein estimators for the mean vector of a multivariate normal population based on independent samples from two normal populations with common covariance structure
Article
Article Title | James-Stein estimators for the mean vector of a multivariate normal population based on independent samples from two normal populations with common covariance structure |
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ERA Journal ID | 32434 |
Article Category | Article |
Authors | Khan, Shahjahan (Author) and Hoque, Zahirul (Author) |
Journal Title | Pakistan Journal of Statistics |
Journal Citation | 18 (3), pp. 359-381 |
Number of Pages | 23 |
Year | 2002 |
Place of Publication | Pakistan |
ISSN | 1012-9367 |
2310-3515 | |
Web Address (URL) | https://www.pakjs.com/1985-to-2016/ |
Abstract | The paper considers shrinkage estimators of the mean vector of a multivariate normal population based on independent random samples from two multivariate normal populations with different mean vectors but common covariance structure. The shrinkage and the positive-rule shrinkage estimators are defined by using the preliminary test approach when uncertain prior information regarding the equality of the two population mean vectors is available. The properties and performances of the estimators are |
Keywords | two-sample problem; uncertain prior information; preliminary test approach; multivariate normal; noncentral chi-square and F-distributions; incomplete beta ratio;bias and quadratic bias; quadratic risk; admissibility |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 490509. Statistical theory |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Department of Mathematics and Computing |
https://research.usq.edu.au/item/9y388/james-stein-estimators-for-the-mean-vector-of-a-multivariate-normal-population-based-on-independent-samples-from-two-normal-populations-with-common-covariance-structure
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