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Microangiopathy (also known as microvascular disease, small vessel disease (SVD) or microvascular dysfunction) is a disease of the microvessels, small blood vessels in the microcirculation. [1] It can be contrasted to macroangiopathies such as atherosclerosis , where large and medium-sized arteries (e.g., aorta , carotid and coronary arteries ...
There are two types of angiopathy: macroangiopathy and microangiopathy. [2] In macroangiopathy, atherosclerosis and a resultant blood clot forms on the large blood vessels, sticks to the vessel walls, and blocks the flow of blood. Macroangiopathy in the heart is coronary artery disease; in the brain, it is cerebrovascular disease.
This method computes the SVD of the bidiagonal matrix by solving a sequence of SVD problems, similar to how the Jacobi eigenvalue algorithm solves a sequence of eigenvalue methods (Golub & Van Loan 1996, §8.6.3). Yet another method for step 2 uses the idea of divide-and-conquer eigenvalue algorithms (Trefethen & Bau III 1997 ...
Damage to small blood arteries is the cause of what called microangiopathy, which may lead to any of these: Diabetic retinopathy , caused by alterations in retinal microcirculation, leading to the growth of friable and poor-quality new blood vessels in the retina or capillary closure which causes ischemia or extravasation of intravascular ...
Named after the Dutch mathematician Bartel Leendert van der Waerden, the Van der Waerden test is a statistical test that k population distribution functions are equal. The Van der Waerden test converts the ranks from a standard Kruskal-Wallis test to quantiles of the standard normal distribution (details given below).
Some aspects can be traced as far back as F. L. Hitchcock in 1928, [1] but it was L. R. Tucker who developed for third-order tensors the general Tucker decomposition in the 1960s, [2] [3] [4] further advocated by L. De Lathauwer et al. [5] in their Multilinear SVD work that employs the power method, or advocated by Vasilescu and Terzopoulos ...
The term normal score is used with two different meanings in statistics. One of them relates to creating a single value which can be treated as if it had arisen from a standard normal distribution (zero mean, unit variance). The second one relates to assigning alternative values to data points within a dataset, with the broad intention of ...
The singular values are non-negative real numbers, usually listed in decreasing order (σ 1 (T), σ 2 (T), …). The largest singular value σ 1 (T) is equal to the operator norm of T (see Min-max theorem). Visualization of a singular value decomposition (SVD) of a 2-dimensional, real shearing matrix M.