covariance


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Related to covariance: Covariance matrix

covariance

[kō′ver·ē·əns]
(statistics)
A measurement of the tendency of two random variables, X and Y, to vary together, given by the expected value of the variable (X-X [OB ])(Y-Y [OB ]), where X [OB ] and Y [OB ] are the expected values of the variables X and Y respectively.
References in periodicals archive ?
Dr Annalisa Molini, a faculty member from the Civil Infrastructure and Environmental Engineering Department, is leading the team of researchers overseeing the eddy covariance monitoring campaign at the Mangrove National Park.
After selection of the best G matrix structure, the G matrix was fixed and the 12 covariance matrices for residual effects (R matrix) were tested.
When Table 1 and Table 2 are examined, it is seen that covariance method gives the lowest value and Yule-Walker method gives the highest value.
The covariance between two coordinates of this random vector is also of interest: If it is bounded, then these two coordinates are asymptotically independent because of the joint normal distribution.
In the proposed iterative ML detection (IMLD) scheme, the instantaneous covariance matrix of the noise and CSI errors is estimated by considering the probabilities of multiple transmit candidate symbol vectors, whereas the published methods [10, 11] use the bound or the long-term statistical information of CSI errors.
Mixed model methodology with random (between and within animals) and fixed (time and treatment) effects is a superior approach not only for identifying variation between-animals but also for defining variation within-animals by specifying many covariance structures with/without missing data in the correlated design (Eyduran et al.
i][beta] and variance and covariance matrixes, according as Equation 2:
Model 5 and Model 6 included additive maternal and maternal permanent environmental effects, ignoring and fitting, respectively, direct maternal covariance.
When the covariance matrices are divided by selected suitable [[mu].
Assume that the initial guesses of the mean and covariance of the state vector at time t = 0 are given by
The covariance is used to measure to what extent two random variables vary together and it is defined as Cov(x,y) = E{[ x - E(x) ][ y - E(y) ]}.
The risk-free rate of return on the risk-free asset is r, an n x 1 vector of the expected excess rates of return is R - r, and the n x n non-singular covariance matrix of risky assets' rates of return is [OMEGA].