Low and high dimensional wavelet thresholds for matrix-variate normal distribution

The matrix-variate normal distribution is a probability distribution that is a generalization of the multivariate normal distribution to matrix-valued random variables. In this paper, we introduce a wavelet shrinkage estimator based on Stein's unbiased risk estimate (SURE) threshold for matrix-...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 54; no. 7; pp. 2742 - 2761
Main Authors Karamikabir, H., Sanati, A., Hamedani, G. G.
Format Journal Article
LanguageEnglish
Published Taylor & Francis 03.07.2025
Subjects
High dimensional
Matrix-variate normal distribution
Shrinkage estimator
SURE threshold
Wavelet shrinkage method
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Summary:The matrix-variate normal distribution is a probability distribution that is a generalization of the multivariate normal distribution to matrix-valued random variables. In this paper, we introduce a wavelet shrinkage estimator based on Stein's unbiased risk estimate (SURE) threshold for matrix-variate normal distribution. We find a new SURE threshold for soft thresholding wavelet shrinkage estimator under the reflected normal balanced loss function in low and high dimensional cases. Also, we obtain the restricted wavelet shrinkage estimator based on non-negative sub matrix of the mean matrix. Finally, we present a simulation study to test the validity of the wavelet shrinkage estimator and two real examples for low and high dimensional data sets.
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2024.2326595