Editing Imputation (statistics) (section)

===Regression===
Regression imputation has the opposite problem of mean imputation. A [[regression model]] is estimated to predict observed values of a variable based on other variables, and that model is then used to impute values in cases where the value of that variable is missing. In other words, available information for complete and incomplete cases is used to predict the value of a specific variable. Fitted values from the regression model are then used to impute the missing values. The problem is that the imputed data do not have an [[error term]] included in their estimation, thus the estimates fit perfectly along the regression line without any residual [[variance]]. This causes relationships to be over-identified and suggest greater precision in the imputed values than is warranted. The regression model predicts the most likely value of missing data but does not supply uncertainty about that value.

[[Stochastic]] regression was a fairly successful attempt to correct the lack of an error term in regression imputation by adding the average regression variance to the regression imputations to introduce error. Stochastic regression shows much less bias than the above-mentioned techniques, but it still missed one thing – if data are imputed then intuitively one would think that more noise should be introduced to the problem than simple residual variance.<ref name="enders2010"/>