Handling missing data with multivariate imputation
Earlier, we discussed the fact that there are two approaches to imputing missing data: univariate imputation and multivariate imputation.
As you have seen in the previous recipes, univariate imputation involves using one variable (column) to substitute for the missing data, disregarding other variables in the dataset. Univariate imputation techniques are usually faster and simpler to implement, but a multivariate approach may produce better results in most situations.
Instead of using a single variable (column), in a multivariate imputation, the method uses multiple variables within the dataset to impute missing values. The idea is simple: Have more variables within the dataset chime in to improve the predictability of missing values.
In other words, univariate imputation methods handle missing values for a particular variable in isolation of the entire dataset and just focus on that variable to derive the estimates. In multivariate imputation...
