In this paper, we use estimates of heritability to compute "predictive accuracy" as r g ^, p / H, where r g ^, p is the correlation between genomic selection estimators of the true breeding values g and the "phenotypic values" in the validation set and H is the square root of heritability.
To evaluate the efficiency of the proposed selection of the estimator parameters as well as the proposed gain flooring scheme, we herein present the performance measures for W β-SA estimator with the parameter scheme in [10], W β-SA estimator using the proposed parameter selection in Section 3 and also the same estimators with the proposed gain flooring in (17).
The simulation study shows that for a moderate sample size and selection probability, the IPW estimator and AIPW estimator perform better than the complete-case estimator.
If there is selection bias, matching estimators are not robust (Rosenbaum 2002).
A comprehensive selection of the estimators allows the interpretation and the assessment of the numerical and graphical results.
Wang and coauthors 2 show that in many situations, some inverse selection probability-weighted estimators are numerically equivalent to imputation.
One of the most appropriate statistics to estimate sexual selection effects is the cross-product estimator (W), which represents the maximum likelihood fitness estimator of one class relative to another [ 4, 7, 19].
Here, we develop an estimator for selection coefficients at candidate loci where a selective sweep has occurred recently.
Here we suggest to apply the subsampling scheme of stability selection to the lasso estimator involved in the RSPCA algorithm to estimate selection probabilities which are then used to identify the truly relevant variables contributing to a PC.
Nielsen et al. [ 17] recently published a likelihood-based estimator of selection on codon usage that may be more suited to detailed studies of selection at loci of interest.
In order to check whether the selection of the variance estimator can affect the meta-analytic results, the analyses were repeated by using a variance estimator based on the restricted maximum likelihood (REML) method.
