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We evaluate the complexity and memory requirements of each variance estimator.
Each variance and each covariance is estimated uniquely from the data.
Each variance was computed as the sum of the error σm intrinsically involved in the measurement process (instrumental errors, etc).
For each variance component that is included in the estimation procedures, variability figures have to be specified.
This results in the best possible model fit, because each variance and covariance value is very close to what the data reflect.
The successively obtained redundant contribution and the weighted residual sum of squares for each variance component are accumulated epoch wise while the KF runs forward.
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Spatial abilities and processing speed each explain less than 3% each of variance in LS.
In the case of losses, the forms of each phase variance necessarily becomes much less appealing.
In each case, variance inflation factors (vif) are calculated to evaluate the severity of multicollinearity.
The noise terms in (3) are independent identically distributed complex zero mean Gaussian samples, each with variance of per dimension.
Each QTL variance and effect, and the total QTL variance were also estimated using analytical formulae.
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