Sentence examples for decrease estimator from inspiring English sources

Through sensitivity analysis, developed in Web Appendix 5 (Web Figures 4 6), we examine how deviations of p ~ i, k from the true value p i, k affect the decided allocation of resources and, as a consequence, decrease estimator precision.

The proportion of truly differentially expressed genes was estimated using the convex decreasing density estimator of Langaas (2005), and the false discovery rate (FDR) was estimated using the method of Storey (2002), with the estimated proportion of truly differentially expressed genes found above inserted.

Based on these p-values, the proportion of genes not differentially expressed was estimated using the convex decreasing density estimator of Langaas et al. [ 48].

The moderated t-test and the convex decreasing density estimator were implemented in the Limma R package available as part of the Bioconductor project [ 50].

By using the convex decreasing density estimator for the proportion of true null hypotheses as presented in Langaas (2005), we expect 5.5% of the genes studied to be differentially expressed in NE vs non-NE cells.

When the strength of the association between the covariates and the treatment/outcome decreased, IPTW estimators remained similarly or even less biased than PS-matching estimators, down to 60 subjects (Table 1).

When the number of SNP used to estimate decreases, the covariance between estimator and the true value decreases and the regression coefficient also decreases; the intercept increases as a direct consequence.

The approximate MSE in (14) for the proposed estimator decreases with increasing transmitting power q, increasing M or decreasing β iik, which means smaller interference level from other cells, i.e., smaller pilot contamination.

In significance testing as the sample size increases, the variance of the estimator decreases, but the magnitude of the parameter being estimated stays the same.

The proposed estimator decreases the limitation of threshold as well.

From (21), holding constant, the minimum error of the LS estimator decreases when increases.