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Autores principales: Fourdrinier, Dominique, Strawderman, William E., Wells, Martin T.
Formato: Preprint
Publicado: 2025
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Acceso en línea:https://arxiv.org/abs/2505.07649
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author Fourdrinier, Dominique
Strawderman, William E.
Wells, Martin T.
author_facet Fourdrinier, Dominique
Strawderman, William E.
Wells, Martin T.
contents The problem of Bayes minimax estimation for the mean of a multivariate normal distribution under quadratic loss has attracted significant attention recently. These estimators have the advantageous property of being admissible, similar to Bayes procedures, while also providing the conservative risk guarantees typical of frequentist methods. This paper demonstrates that Bayes minimax estimators can be derived using integral transformation techniques, specifically through the \( I \)-transform and the Laplace transform, as long as appropriate spherical priors are selected. Several illustrative examples are included to highlight the effectiveness of the proposed approach.
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publishDate 2025
record_format arxiv
spellingShingle Constructing Bayes Minimax Estimators through Integral Transformations
Fourdrinier, Dominique
Strawderman, William E.
Wells, Martin T.
Statistics Theory
The problem of Bayes minimax estimation for the mean of a multivariate normal distribution under quadratic loss has attracted significant attention recently. These estimators have the advantageous property of being admissible, similar to Bayes procedures, while also providing the conservative risk guarantees typical of frequentist methods. This paper demonstrates that Bayes minimax estimators can be derived using integral transformation techniques, specifically through the \( I \)-transform and the Laplace transform, as long as appropriate spherical priors are selected. Several illustrative examples are included to highlight the effectiveness of the proposed approach.
title Constructing Bayes Minimax Estimators through Integral Transformations
topic Statistics Theory
url https://arxiv.org/abs/2505.07649