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In section Covariance matrices we discuss methods that estimate the covariance matrix.
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In this paper, using the methods of linear algebra and matrix theory, we discuss the admissibility of linear estimators in model (1.1) under the matrix loss (1.2).
One such problem is that of computing the PageRank of the Google matrix, which we discuss in detail.
Before masking, we provide results only for selected matrix families that we discuss in the text.
The details of the low-pass filter is shown in Section 5. Since the matrix B is fixed, we discuss the sensitivity of our method to the errors in the matrix B in Section 5.
The magnitude and even the direction of this actuation critically depend on the degree of tube alignment in the matrix; in this context, we discuss the affine model predicting the upper bound of orientational order of nanotubes, induced by an imposed strain.
Taking random matrix as measurement matrix, we also discuss the advantage of our condition.
We discuss how matrix effects can be identified, quantified, reduced and corrected.
In this review we discuss how matrix metalloprotease activation and inhibition can be simultaneously important, how increased apoptosis and relative resistance to apoptosis co-exist and whether the lung in IPF favors or inhibits new vessel formation.
Based on this model, we discuss the resulting hat matrix in Section "Identifying drivers via the hat matrix", which we use as an instrument for identifying drivers.
Finally, we discuss the types of matrices that arise in engineering calculations and their common structures.
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