Sentence examples for shrinking variance from inspiring English sources

Similar to ANOVA and F-test, FS test is based on generalized linear model to estimate shrinking variance to determine DEGs.

In several papers, EB methods were designed to stabilize the expression values/ratios for genes with extreme values or else the variance of genes or gene groups by shrinking variances across all other genes, possibly diminishing the effects of artifacts in the data [ 6, 12- 19].

All statistical tests were conducted with a modified F-statistic (Fs) that that shrinks variance components based on information from all the probe sets on the array [ 82].

Whereas GBLUP and EMMAX assume the infinitesimal model under which all SNP ASEs are drawn from a distribution with constant variance [ 33, 34], BayesCπ begins with a distribution with constant variance but shrinks the variance for small effect SNPs that are rarely fit in the model.

Our results (shown in Supplementary Materials) confirmed that shrinking the variance leads to inferior performance.

This capability of shrinking the variance across all genes has been shown in several publications [ 36- 43].

One well-known solutions to these problems is LASSO, which shrinks the variance of the coefficients and makes other coefficients zero [ 19].

Given that carotenoid traits are likely largely explained by a small number of moderate- to large-effect loci (Wong et al. 2004; Chander et al. 2008; Kandianis et al. 2013), we hypothesized that a variable selection method that shrinks the variance explained by noncontributing markers to near or equal to zero, such as LASSO or elastic net analysis, would show higher predictive ability.

Under this hierarchical model, the posterior mean of σ g −2 given s g is The posterior variances shrink the observed variances towards the prior value with the degree of shrinkage depending on the relative sizes of the observed and prior degrees of freedom.

To improve the stability and accuracy of these variance estimates there have been many methods proposed to shrink the variances to some common value for microarrays [ 12] and RNA-Seq [ 1].

Furthermore, Autocorrelation (ACF) and partial autocorrelation (PACF) were evaluated to avoid confounding of the risk estimates by unknown sources and shrinking of the variance associated with parameter estimates.