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First, a simple model with two variance components (an additive polygenic component and a individual specific environmental component) was used on data that were appropriately normalized to accommodate the assumptions of the model (see Material & Methods section).
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Here, all the B-frames need not be transmitted and they can be correctly reconstructed with zero variance information.
In earlier work, we designed an estimator that converged to the desired value with a finite variance and here, we derive a different estimator with zero variance.
After removing those descriptors with zero variance or that cannot be calculated for some compounds, altogether 286 descriptors were remained (hereafter called "DES").
Columns with zero variance and highly correlated predictors (which exceeded a Pearson's correlation coefficient of 0.9) were removed to lower multicollinearity.
Variables with zero variance were removed prior to training thus removing 906 variables for the Novartis set and 956 for Set B, and variables were mean-centered and variance-scaled at each training step.
The voxels in each ROI were further processed to exclude voxels which had zero variance across parameter estimate images in any subject because voxels with zero variance cannot contribute classification information.
Second, because it associates zero power with zero variance in effect sizes, it implies that rejection of neutrality would become increasingly probable with increased variance of QTL effects.
The hyperparameters, i.e. the variances, are estimated through maximizing the marginal likelihood, and the variables with zero variance estimates are pruned from the model.
BayesR assumes that SNP effects are drawn from a mixture of normal distributions, one with zero variance (and hence zero effects).
To reduce the number of dimensions and to ensure we represent our structures in a space with independent dimensions, we readily eliminate dimensions with zero variance.
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