Wavelet Thresholds for Matrix-Variate Normal Distribution Under The Reflected Normal Loss
Publish place: 5th International Conference on Software Computing
Publish Year: 1402
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CSCG05_083
تاریخ نمایه سازی: 9 اردیبهشت 1403
Abstract:
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 loss function in low 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.
Keywords:
Matrix , variate normal distribution , Shrinkage estimtion , SURE threshold , Wavelet shrinkage , Reflected normal loss function
Authors
Hamid Karamikabir
Faculty of Intelligent Systems Engineering and Data Science, department of Statistics, Persian Gulf UniversityBushehr
Fatemeh Jamhiri
Faculty of Intelligent Systems Engineering and Data Science, department of Statistics, Persian Gulf University,Bushehr
Mahmoud Afshari
Faculty of Intelligent Systems Engineering and Data Science, department of Statistics, Persian Gulf University,Bushehr