Contents
What does imputation mean in statistics?
Imputation is a procedure for entering a value for a specific data item where the response is missing or unusable. Context: Imputation is the process used to determine and assign replacement values for missing, invalid or inconsistent data that have failed edits.
What is the purpose of imputation?
Imputation preserves all cases by replacing missing data with an estimated value based on other available information. Once all missing values have been imputed, the data set can then be analysed using standard techniques for complete data.
What is bagged tree imputation?
A bagged tree is constructed in a similar fashion to a random forest. The primary difference is that in a bagged model, all predictors are evaluated at each split in each tree. The performance of a bagged tree using 25–50 trees is generally in the ballpark of the performance of a random forest model.
How is single imputation different from multiple imputation?
Single imputation does not reflect the uncertainty about the prediction of the missing values. Multiple imputation was proposed by Rubin (1987). In this method D imputed values for each of the missing observation is generated and hence we get D complete data set.
How to avoid perfect prediction during imputation of categorical data?
This tells mi impute chained to use the “augmented regression” approach, which adds fake observations with very low weights in such a way that they have a negligible effect on the results but prevent perfect prediction. For details see the section “The issue of perfect prediction during imputation of categorical data” in the Stata MI documentation.
Are there any problems with multiple imputation in Stata?
Perfect prediction is another problem to note. The imputation process cannot simply drop the perfectly predicted observations the way logit can. You could drop them before imputing, but that seems to defeat the purpose of multiple imputation. The alternative is to add the augment (or just aug) option to the affected methods.
Which is the best method for imputation of data?
In this section, we compare the performance of several single imputation methods: CC, AC, UMI, CMI, stochastic regression imputation (SRI), and the EM method based on some simulated data sets.