Sentence examples for an optimal imputation from inspiring English sources

An optimal imputation model would have fewer variables, but including up to 1/3 would not do much harm (to continuous variables, see below).

We employ a cross-validation (CV) procedure and FOA swarm intelligence optimization strategy that can search available parameters and determine the optimal imputation model.

In such a case, the use of pre-phased haplotypes will not lead to optimal imputation accuracy for the target group.

This implies that there may be situations where multiple imputation is not the optimal imputation method.

Integrating the results of the previous studies with our experiences, it strongly suggests that the optimal imputation algorithms for different types of datasets may be different.

Ultimately, however, the optimal imputation method in any situation should be selected based on a balance of the statistical expertise of the research team, validity of the method, and ease of interpretability for readers.

Equations analogous to the ones derived in this article can be computed for the more complicated model and used to determine the optimal imputation reference panel when several external panels are available.

One conclusion from comparing scenarios B and C to scenarios D and E is that for low density imputation (especially 3 k or sparser to 50 k), the genotypes of female ancestors and the availability of pedigree information are very important in order to achieve optimal imputation accuracy.

Differences in imputation accuracies between purebred and crossbred animals were investigated to set up an optimal strategy for imputation from a low-density (8 K) to a medium-density (60 K) SNP chip in crossbred pigs and results were validated using a simulated dataset of crossbred medium-density (60 K) genotypes.

This indeed hints to the possibility to enhance the imputation accuracy via an optimal composition of the reference population.

The study examines the two predictive data mining approaches: decision tree and MLP with cluster-based missing value imputation method in search for an optimal model capable of performing more accurate and sensitive disease diagnosis.