Geodesic
Minimax shrinkage estimators and estimators dominating the James-Stein estimator under the balanced loss function
Eurasian mathematical journal, Tome 13 (2022) no. 2, pp. 18-36

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This paper is dealing with the shrinkage estimators of a multivariate normal mean and their minimaxity properties under the balanced loss function. We present here two different classes of estimators: the first which generalizes the James-Stein estimator, and show that any estimator of this class dominates the maximum likelihood estimator (MLE), consequently it is minimax, and the second dominates the James-Stein estimator and we conclude that any estimator of this class is also minimax. 
@article{EMJ_2022_13_2_a1,
     author = {A. Benkhaled and A. Hamdaoui and M. Terbeche},
     title = {Minimax shrinkage estimators and estimators dominating the {James-Stein} estimator under the balanced loss function},
     journal = {Eurasian mathematical journal},
     pages = {18--36},
     publisher = {mathdoc},
     volume = {13},
     number = {2},
     year = {2022},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/EMJ_2022_13_2_a1/}
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A. Benkhaled; A. Hamdaoui; M. Terbeche. Minimax shrinkage estimators and estimators dominating the James-Stein estimator under the balanced loss function. Eurasian mathematical journal, Tome 13 (2022) no. 2, pp. 18-36. http://geodesic.mathdoc.fr/item/EMJ_2022_13_2_a1/