Sentence examples for multiple summary statistics from inspiring English sources

Approximate Bayesian and summary statistic methods including multiple summary statistics (e.g. both temporal F and LD) could greatly improve precision and accuracy of N e estimators (Tallmon et al. 2008; Luikart et al. 2010), especially as large population genomic data sets become common making likelihood-based methods even computationally demanding to evaluate (Luikart et al. 2003).

This indicates that our results are determined by multiple summary statistics rather than a single one.

Padhukasahasram et al. (2006) suggested using multiple summary statistics from SNP data to estimate crossover and gene conversion rates jointly.

Multiple summary statistics for the resulting confusion tables (Sensitivity (Recall), Specificity, Precision, Accuracy, F-measure, and Matthew's Correlation Coefficient (MCC)) were calculated.

Where only summary statistics are available, the analyst performing pairwise or network meta-analysis may be faced with multiple summary statistics for a given endpoint.

In contrast, ABC is a highly robust method due to its ability to use the variances from multiple summary statistics, instead of single variance components used by other N e approaches.

A study exploring the potential of imputing missing data in THIN found that after multiple imputation, summary statistics of height and weight were comparable with data from nationally representative datasets.

Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge.

A practical implication of this is that we can improve the accuracy of genetic predictors without the need to access large-scale individual-level data sets, and we can potentially combine multiple predictors based on different summary statistics, e.g. combine risk scores based on multiple GWAS studies if a GWAMA is not available.

Here, we describe a multi-SNP association method that estimates the effect of loci harboring multiple association signals by using GWAS summary statistics.

Equally important, using multiple estimates will bias standard errors of summary statistics, with understatement of errors being the generally expected outcome [[7], p. 226].