postimputation

Postimputation is a statistical technique used to improve the accuracy of estimates in missing data scenarios. Missing data occurs when values for specific variables are not recorded or are unknown. Traditional methods, such as listwise or pairwise deletion, can lead to biased estimates and reduced statistical power, whereas postimputation can help mitigate these issues.

The postimputation process typically involves two primary steps: imputation and validation. Imputation is the method of estimating the missing values using statistical models, whereas validation involves evaluating the quality of the imputed data. Multiple imputation methods may be employed to accommodate the variety of missing data mechanisms that may have contributed to the missingness.

In essence, postimputation is a pre-processing technique aiming to address the issues arising from missing values prior to analysis. It should be distinguished from imputation, which singly refers to the replacement of the missing values. While imputation focuses solely on filling in the blanks, postimputation takes a holistic approach by offering principles and rules to efficiently use the provided data when knowledge about the data is scarce or biased.

Postimputation could improve the precision of statistical models by examining the generated data produced through the procedure as an alternative. While multiple imputation methods can solve the problem, they may cause the weights of data to deviate from true values, thereby affecting the accuracy of the analysis. By weighing the merits of various techniques, analysts can choose the most suitable approach that achieves the best balance between accuracy and the increase in necessary complexity.

Studies suggest that postimputation can retain the advantages of imputation methods while reducing biases relatively.