Sentence examples for variance shrinkage from inspiring English sources

Other strategies improve inference accuracy through variance shrinkage that accounts for statistical dependencies between genes via computationally intensive permutation testing to accurately specify the null distribution.

This implies that: This is exactly true for the variability option and approximately true for the other limma-based options where ν= ν⋆, which is generally large due to variance shrinkage.

For the analysis using limma, empirical Bayes variance shrinkage was employed and genes were defined as being differentially expressed after satisfying a minimum fold-change of ± 1.5 and a maximum, Benjamini-Hochberg adjusted, p-value of 0.01.

The first two options begin with standard linear modelling using limma[ 43], which uses selected factors and covariates, and variance shrinkage, to fit a linear model to all CpG sites in parallel.

Hence, variance shrinkage (as occurs in the hierarchical Bayesian regression models) or Bayesian regularization (as occurs in BRANN) plays a crucial role in attenuating "over-fitting" and attaining reproducible predictive performance.

TileProbe models the residual probe effects in t igk using two quantities θ i and τ i which are determined as follows: (3) (4) (5) Here is the mean of all ω i, and B ∈ [0, 1] is a shrinkage factor determined by the variance shrinkage estimator used in TileMap (Ji and Wong, 2005): (6) In the formula above, N is the total number of probes.

Indeed, several Bayesian methods proposed for residual variances shrinkage [ 31, 32] can be applied to both residual variances and heterogeneity parameters, and the calculation of the Bayes factor does not substantially change within-gene or within a group of genes.

Multiple regression models with terms for specific surface area and clay content could explain 76% and 81% of the variance in shrinkage determined by COLErod and COLEstd, respectively.

All statistical tests were performed using Fs, a modified F-statistic incorporating shrinkage variance components (Cui et al., 2005).

Based on values of spatial indicators, wavelet coefficients and locally estimated noise variance, we perform wavelet shrinkage of depth wavelet coefficients.

This model allows each marker to have its own variance, resulting in different shrinkage of SNP effects.