Sentence examples for limit imputation from inspiring English sources

Altogether, the conservative semi-quantitative reporting threshold and min/max analysis for qualitative biochemicals were employed to limit imputation bias and focus interpretation on patterns from biochemicals within a detectable range.

The number of LIBCSP respondents for whom traffic B[ a]P estimates were available (520 1,274 case participants; 566 1,334 control participants) varied according to the exposure definition and the imputed data set (some women exceeded the limit on imputation percentage in certain imputation draws), and the participants showed a wide range of exposure levels (Table 1).

However, new HD genotypes are likely required in the future generations in order to maintain relationship links between the reference and target populations and limit the imputation error rate.

Nonetheless, even when the number of covariables is limited, multiple imputation remains the least biased approach for handling missing data (Nur et al, 2010).

We usually put rough limits on imputation-related assumptions.

We partly addressed this limit using multiple imputation that permits to correct the bias for missing at random information.

Impute: impute: Imputation for microarray data.

The main goal of our study was to investigate the implementation, prospects, and limits of marker imputation for quantitative genetic studies contrasting map-independent and map-dependent algorithms.

The main goal of our study was to investigate the implementation, prospects, and limits of marker imputation for quantitative genetic studies using experimental data collected from a representative sample of European wheat lines [ 14].

When presenting the sensitivity analyses, we limit the presentation to imputation A together with the related variants of sensitivity datasets.

They should also check whether important predictors are recorded in each study, and evaluate the amount of missing data for available predictors; though multiple imputation can limit these issues, studies with multiple missing predictors or large proportions of missing values might be best excluded for robustness (unless imputation assumptions can be justified).