Schwartz et al. [1] illustrated an experimental design and methods to estimate these variance components.
A nested ANOVA design has been applied in order to quantify the variances introduced by these variance sources.
Simultaneously analyzing data from multiple groups is only advantageous when at least one of these variance components can be assumed to be homogeneous across the groups.
These variance parameters are then used to obtain a weighted least squares estimate of the regression parameters of a linear model.
Changes in these variance components under varying selection intensities were investigated in this study to formulate the effects of selection on various variance components.
We also present several applications of these variance inequalities, including reverse Hölder inequalities for convex functions, weighted Brascamp Lieb inequalities and sharp weighted Poincaré inequalities for generalized Cauchy measures.
These variance measurements have been made with samples of quite modest size, which should be a source of concern because higher-order statistics, such as variance, are poorly estimated from small sample sizes.
To clearly explore the contributions by the correlated inputs to the variance of model output, the general expressions of the variance contributions of the correlated inputs are firstly derived in this paper, which provide a general validation for the accurate connotations of these variance contributions.
The values of these variance reduction coefficients confirm that no site proxy can be preferred over the whole frequency range.
More details about these variance and autocorrelation functions are given in Auty et al. ([2013]) and Jordan et al. ([2005]).
These variance stabilized data was used for clustering the transcripts.
