Sentence examples for provides shrinkage from inspiring English sources

Thus, it provides shrinkage and automatic variable selection and can handle more efficiently with the severe multicollinearity that often exists in GWA analysis.

We present a general Bayesian model for analyzing diallel data on dioecious diploid inbred strains that cleanly decomposes the observed patterns of variation into biologically intuitive components, simultaneously models and accommodates outliers, and provides shrinkage estimates of effects that automatically incorporate uncertainty due to imbalance, missing data, and small sample size.

These estimators provide shrinkage towards the overall mean and can be of substantial benefit when incorporating low-volume providers in the analysis.

We demonstrate a principled approach, LASSO local automatic regularization resample model averaging (LLARRMA), that characterizes sensitivity of locus choice to sampling variability and uncertainty due to missing genotype data, and that provides LASSO shrinkage automatically regularized through either predictive- or discovery-based criteria.

Monomers with epoxide ring-opening polymerization-type chemistries, such as silorane-based monomers, have been proposed to provide less shrinkage compared to monomers with free-radical, vinyl polymerization-type chemistries [8].

Bootstrapping provided the shrinkage factor that was used for the regression coefficients [ 21].

A linear model was fitted to the data using "lmFit", and "eBayes" provided error shrinkage towards a pooled estimate of variation [ 52].

Bayesian methods can provide some shrinkage and spatial smoothing of raw standardized incidence ratio estimates, which are strongly influenced by the size of the population at risk, resulting in a noisy and blurred picture of the true unobserved risks [ 17].

The main characteristic of all these models is to provide some shrinkage and spatial smoothing of the raw relative risk estimates that otherwise would be computed separately in each area.

It is combined with the individual standard deviation for each gene to provide the shrinkage error estimate used in Eq. (2) (3) σ g, m shr = λ · σ g, m 2 + 1 − λ · σ LPE e g, m 2 The parameter λ (0 ≤ λ ≤ 1) scales the degree of shrinking σ g, m towards σ LPE.

This clearly indicates that BayesB gives more emphasis to informative SNPs that are near the QTL than does BayesC as a result of its ability to provide greater shrinkage to SNPs with small effects since these SNPs have larger variance ratios than large effect markers.