Finding extreme values for imputation
Replacing missing values with a value at the end of the variable distribution (extreme values) is equivalent to replacing them with an arbitrary value, but instead of identifying the arbitrary values manually, these values are automatically selected as those at the very end of the variable distribution. Missing data can be replaced with a value that is greater or smaller than the remaining values in the variable. To select a value that is greater, we can use the mean plus a factor of the standard deviation, or the 75th quantile + (IQR * 1.5), where IQR is the IQR given by the 75th quantile - the 25th quantile. To replace missing data with values that are smaller than the remaining values, we can use the mean minus a factor of the standard deviation, or the 25th quantile – (IQR * 1.5).
Note
End-of-tail imputation may distort the distribution of the original variables, so it may not be suitable for linear models.
In this recipe, we...
