There is also an imputation disambiguation page.

In statistics, imputation is the substitution of some value for a missing data point or a missing component of a data point. Once all missing values have been imputed, the dataset can then be analysed using standard techniques for complete data. While many imputation methods are available, two of the most commonly used are hot-deck imputation and regression imputation.

However since standard analysis techniques do not reflect the additional uncertainty due to imputing for missing data, further adjustments (such as multiple imputation or a Rao-Shao correction) are necessary to account for this.

Imputation is not the only method available for handling missing data. It usually gives better results than listwise deletion (in which all subjects with any missing values are omitted from the analysis), and may be competitive with a maximum likelihood approach in many circumstances.

External links

• Missing Data: Instrument-Level Heffalumps and Item-Level Woozles
• Multiple-imputation.com
• Multiple imputation FAQs, Penn State U
• pdf A description of hot deck imputation from Statistics Finland.
• pdf Paper extending Rao-Shao approach and discussing problems with multiple imputation.