Sentence examples for multiple imputation inference from inspiring English sources

Multiple imputation inference procedures are applied to the resulting multiply imputed complete data sets.

After MI, each of the simulated complete datasets was analyzed by standard methods, and the results combined through the process of multiple imputation inference to obtain parameter estimates (AORs) and CIs that incorporate missing-data uncertainty (SAS PROC MIANALYZE) [ 36].

Each draw represents a random sample of the missing values and is then used for multiple-imputation inference such that a number of data sets (equal to the number of draws) of 'complete' cases are analysed using standard statistical analyses.

In previous work (Graffelman et al. 2013) we described multiple imputation for inference on HWE based on the inbreeding coefficient.

Single and multiple imputation can improve inference on equilibrium.

First we outline our multiple imputation approach for inference on HWE with missings.

Multiple imputation can provide valid inference given any of the above mechanisms, although standard software implementations impute assuming MAR (MCAR) by default.

While the primary goal of multiple imputation is to obtain valid inferences, and imputed values are not intended to replace the missing data [ 4], there is some uncertainty as to how to treat imputed values that fall outside the limits of the variable.

It is precisely for this subset that single and multiple imputation can provide improved statistical inference.

To reflect uncertainty and to enable valid inferences to be made, any missing data will be imputed using multiple imputation.

Multiple imputation results in valid statistical inferences that properly reflect uncertainty due to missing values (Schafer, 1997).