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],