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For the first time, a Variance Components Estimation (VCE) technique is used to optimize the weights of quasi-observations when information on their observed precision is lacking.
In order to evaluate the total harmonic distortion caused by particular nonlinear loads in power systems, the harmonic current components estimation becomes a critical issue.
The most popular methods of variance components estimation are maximum likelihood (ML), restricted maximum likelihood (REML), analysis of variance and covariance (ANOVA) and minimum quadratic norm (MINQUE).
Both variables are found to be negatively related to bilateral exports in the joint components estimation.
This makes the number of components estimation based on the global Rényi entropy ineffective for real-world signals.
Moving target detection and velocity components estimation are the two main tasks of GMTI in SAR [2, 3].
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As a consequence, variance component estimation will explicitly inflate estimates of the direct and maternal additive genetic variances, with a magnitude close to twice and one half the paternally inherited variance, respectively, and it will reduce the direct-maternal genetic covariance by a value equal to the paternally inherited variance.
The weights for different observation groups are determined through the variance component estimation (VCE) approach, and unknown coefficients are estimated through the least squares adjustment.
An estimate of V g could be obtained easily using the GBLUP model and a variance component estimation, which would provide an approximately unbiased estimate.
We have explored the accuracy and speed of variance component estimation using DHGLM but the algorithm also produces estimated breeding values.
To estimate the heritabilities for the NCD and lnNCD, the variance components were estimated by restricted maximum likelihood (REML), using REML variance component estimation (VCE4) [ 17].
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