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P-values for each method were calculated by the moderated F-statistic which stabilizes the gene-specific variance using an empirical Bayes method to shrink the probe-wise sample variances toward a common value and to augment the degrees of freedom for the individual variances [ 35].
Empirical Bayes statistics was applied to shrink the probe-wise sample variances toward a common value, which employed moderated t-statistics test and the moderated F-statistic tests analogous to conventional ANOVA except moderation of residual mean squares and residual degrees of freedom between probes.
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The parameter τ j controls the overall variance explained by factor j by shrinking the variance toward zero as m → ∞.
Constraints on the number of nuclei that can enter the germ plasm or on the number that can migrate to the pole at the appropriate time could bias the potential for variance toward reduction of PGC number.
To analyze the obtained data, all samples were normalized to an internal reference gene (GAPDH) to eliminate sample variances and toward a sample transfected with a vector containing a nonsense shRNA construct to eliminate effects from transfection.
The dotted line indicates the saturation level where log-amplitude variance tends toward a constant.
When the number of images increases, this variance decreases toward low enough values for feature-based steganalysis and performances comparisons.
As a result, the tendency to revert to a mean rate grows weaker and the firing rate variance increases toward infinity.
As the time of admixture occurs further back in the past, the peak in wavelet variance shifts toward successively lower wavelet scales, reflecting ever-smaller admixture blocks driven by cumulative recombination along the chromosome.
The genomic relationship matrix was then calculated as: G = Z Z ′ 2 ∑ i = 1 M p i (1 − p i ) (1 where 2 ∑ i = 1 M p i (1 − p i ) is a normalizing constant (Wang et al. 2012) summing expected variances across markers scaling G toward the numerator relationship matrix (VanRaden 2008).
The explained variance of the latent variable Students' Attitude Toward Reading differs by 27% for boys in France and Germany and 14% for girls in Italy.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com