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Multiple imputation consists of replacing missing values with a set of plausible values, based on auxiliary information.
Imputation was done by replacing missing values with the mean of the data, restoring the original sample of N = 296.
In order to improve the accuracy of air quality forecasting, there are usually three approaches to deal with missing data before modeling: (1) deleting sample instances with missing data; (2) replacing missing values; and (3) using multiple imputations.
29 32 34 The imputation technique involves creating multiple copies of the data and replaces missing values with imputed values based on a suitable random sample from their predicted distribution.
35 36 37 The imputation technique involves creating multiple copies of the data and replaces missing values with imputed values on the basis of a suitable random sample from their predicted distribution.
We did not replace missing values in exposures variables by imputation.
For the adjusted analyses of venous thromboembolism rate by admission, we replaced missing values for BMI using multivariate normal imputation.
While there are several techniques for handling missing data in general, the imputation scheme, which replaces missing values with predicted values, is preferred since this scheme can be followed by a standard fusion scheme designed for complete data.
We used multiple imputation to replace missing values for infant nutrition data.
If appropriate, we will use multiple imputation to replace missing values at baseline or follow-up.
In this post-hoc analysis we used multiple imputation to replace missing values for infant nutrition data.
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