A New Class of Bayes Minimax Estimators of the Mean Matrix of a Matrix Variate Normal Distribution
Bayes minimax estimation is important because it provides a robust approach to statistical estimation that considers the worst-case scenario while incorporating prior knowledge. In this paper, Bayes minimax estimation of the mean matrix of a matrix variate normal distribution is considered under the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/7/1098 |
| _version_ | 1797212256363085824 |
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| author | Shokofeh Zinodiny Saralees Nadarajah |
| author_facet | Shokofeh Zinodiny Saralees Nadarajah |
| author_sort | Shokofeh Zinodiny |
| collection | DOAJ |
| description | Bayes minimax estimation is important because it provides a robust approach to statistical estimation that considers the worst-case scenario while incorporating prior knowledge. In this paper, Bayes minimax estimation of the mean matrix of a matrix variate normal distribution is considered under the quadratic loss function. A large class of (proper and generalized) Bayes minimax estimators of the mean matrix is presented. Two examples are given to illustrate the class of estimators, showing, among other things, that the class includes classes of estimators presented by Tsukuma. |
| first_indexed | 2024-04-24T10:39:30Z |
| format | Article |
| id | doaj.art-d538a812920a4b35bcac757303e020e9 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-04-24T10:39:30Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-d538a812920a4b35bcac757303e020e92024-04-12T13:22:52ZengMDPI AGMathematics2227-73902024-04-01127109810.3390/math12071098A New Class of Bayes Minimax Estimators of the Mean Matrix of a Matrix Variate Normal DistributionShokofeh Zinodiny0Saralees Nadarajah1Department of Mathematics, Amirkabir University of Technology, Tehran 15916-34311, IranDepartment of Mathematics, University of Manchester, Manchester M13 9PL, UKBayes minimax estimation is important because it provides a robust approach to statistical estimation that considers the worst-case scenario while incorporating prior knowledge. In this paper, Bayes minimax estimation of the mean matrix of a matrix variate normal distribution is considered under the quadratic loss function. A large class of (proper and generalized) Bayes minimax estimators of the mean matrix is presented. Two examples are given to illustrate the class of estimators, showing, among other things, that the class includes classes of estimators presented by Tsukuma.https://www.mdpi.com/2227-7390/12/7/1098Bayes estimationmatrix variate normal distributionmean matrixminimax estimation |
| spellingShingle | Shokofeh Zinodiny Saralees Nadarajah A New Class of Bayes Minimax Estimators of the Mean Matrix of a Matrix Variate Normal Distribution Mathematics Bayes estimation matrix variate normal distribution mean matrix minimax estimation |
| title | A New Class of Bayes Minimax Estimators of the Mean Matrix of a Matrix Variate Normal Distribution |
| title_full | A New Class of Bayes Minimax Estimators of the Mean Matrix of a Matrix Variate Normal Distribution |
| title_fullStr | A New Class of Bayes Minimax Estimators of the Mean Matrix of a Matrix Variate Normal Distribution |
| title_full_unstemmed | A New Class of Bayes Minimax Estimators of the Mean Matrix of a Matrix Variate Normal Distribution |
| title_short | A New Class of Bayes Minimax Estimators of the Mean Matrix of a Matrix Variate Normal Distribution |
| title_sort | new class of bayes minimax estimators of the mean matrix of a matrix variate normal distribution |
| topic | Bayes estimation matrix variate normal distribution mean matrix minimax estimation |
| url | https://www.mdpi.com/2227-7390/12/7/1098 |
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