How multiple imputation helps in filling the missing value of a variable?



I am currently solving one classification problem while solving the problem I found that the data set contains two variable in which there are missing values.While searching the techniques for filling the missing values, I found the techniques multiple imputation .

Multiple imputation is a statistical technique for analyzing incomplete data sets, that is, data sets for which some entries are missing. Application of the technique requires three steps: imputation, analysis and pooling.

I am not able to understand it, any help would be great


@harry - I think that you are not able to understand the steps.

Imputation: Impute (=fill in) the missing entries of the incomplete data sets, not once, but m times. Imputed values are drawn for a distribution (that can be different for each missing entry). This step result is m complete data sets.

Analysis: Analyze each of the m completed data sets. This step results in m analyses.

Pooling: Integrate the m analysis results into a final result. Simple rules exist for combining the m analyses.

Hope this helps!