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In many practical problems, the underlying structure of an estimated covariance matrix is usually blurred due to random noise, particularly when the dimension of the matrix is high. Hence, it is necessary to filter the random noise or regularize the available covariance matrix in certain senses, so that the covariance structure becomes clear. In this paper, we propose a new method for regularizing...
In many applications, high-dimensional problem may occur often for various reasons, for example, when the number of variables under consideration is much bigger than the sample size, i.e., p >> n. For highdimensional data, the underlying structures of certain covariance matrix estimates are usually blurred due to substantial random noises, which is an obstacle to draw statistical inferences...
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