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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 ...
In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix into a rotation, followed by a rescaling followed by another rotation. It generalizes the eigendecomposition of a square normal matrix with an orthonormal eigenbasis to any matrix.
The strategy for computing the Multilinear SVD and the M-mode SVD was introduced in the 1960s by L. R. Tucker, [3] further advocated by L. De Lathauwer et al., [5] and by Vasilescu and Terzopulous. [ 8 ] [ 6 ] The term HOSVD was coined by Lieven De Lathauwer, but the algorithm typically referred to in the literature as HOSVD was introduced by ...
Medical calculators arose because modern medicine makes frequent use of scores and indices that put physicians' memory and calculation skills to the test. [2] The advent of personal computers, the Internet and Web, and more recently personal digital assistants (PDAs) have formed an environment conducive to their development, spread and use.
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 ...
In linear algebra, the generalized singular value decomposition (GSVD) is the name of two different techniques based on the singular value decomposition (SVD).The two versions differ because one version decomposes two matrices (somewhat like the higher-order or tensor SVD) and the other version uses a set of constraints imposed on the left and right singular vectors of a single-matrix SVD.
These are called normal scores and the test is computed from these normal scores. The k population version of the test is an extension of the test for two populations published by Van der Waerden (1952,1953).
In many situations, the score statistic reduces to another commonly used statistic. [11] In linear regression, the Lagrange multiplier test can be expressed as a function of the F-test. [12] When the data follows a normal distribution, the score statistic is the same as the t statistic. [clarification needed]