# matrix

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Given the expected excess rate of return vector R - r on n risky securities and the non-singular covariance matrix [OMEGA] between n risky securities rate of returns, the portfolio [omega] in Equation (1) is the unique risky optimal mean-variance efficient within the mean-variance framework if and only if [omega] = [[OMEGA].
To obtain the covariance matrix, univariate models are estimated and standardized residuals are computed, which in turn serve as the basis for calculating linear correlation coefficients.
However, the conceptually correct error terms (u) are the deviations of the actual values of y from the actual (not predicted) values of x, which if uncorrected results in a bias in the computation of the covariance matrix of the estimator.
The comparative fit index (CFI) has similar attributes to the NFI and compares the predicted covariance matrix to the observed covariance matrix and is least affected by sample size.
To improve small sample properties of the covariance matrix we use regularized covariance matrix:
After determining the stationary pattern, covariance function is chosen to construct the covariance matrix.
Eigenface Core function function [m, A, Eigenfaces] = Eigenface Core(T) m = mean (T, 2); % average picture/line averaging (seek the average of each pair of images corresponding pixel) m=(1/ P)*sum (T"s) (j = 1 : P) Train_Number = size (T, 2);% the number of columns % calculate each image to the variance of the picture mean A = []; for i = 1 : Train_Number% for each column temp = double (T (:, i)) - m; % difference between each one chart and the mean A = [A temp]; % covariance matrix end % Dimensionality reduction L = A'*A; % L is the covariance matrix C = A*A' transpose.
j]) is the covariance matrix of operating profit margin from region i and j.
For two input vectors x(i), x(j), the covariance matrix V(X, X, [eta]) is defined by Equation 2, in which i,j = 1, .
The other is applying the derivation of Covariance Matrix of equation (4) in Merged Expectation and Maximization.
The covariance matrix associated with the deviation torsor of the surface S expressed at point [O.
T has covariance matrix [summation over (term)] [epsilon] [epsilon],
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