The MI Procedure
- Overview
- Getting Started
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Syntax
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Details
Descriptive Statistics EM Algorithm for Data with Missing Values Statistical Assumptions for Multiple Imputation Missing Data Patterns Imputation Methods Monotone Methods for Data Sets with Monotone Missing Patterns Monotone and FCS Regression Methods Monotone and FCS Predictive Mean Matching Methods Monotone Propensity Score Method Monotone and FCS Discriminant Function Methods Monotone and FCS Logistic Regression Methods FCS Methods for Data Sets with Arbitrary Missing Patterns Checking Convergence in FCS Methods MCMC Method for Arbitrary Missing Multivariate Normal Data Producing Monotone Missingness with the MCMC Method MCMC Method Specifications Checking Convergence in MCMC Input Data Sets Output Data Sets Combining Inferences from Multiply Imputed Data Sets Multiple Imputation Efficiency Imputer’s Model Versus Analyst’s Model Parameter Simulation versus Multiple Imputation Summary of Issues in Multiple Imputation ODS Table Names ODS Graphics
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Examples
EM Algorithm for MLE Monotone Propensity Score Method Monotone Regression Method Monotone Logistic Regression Method for CLASS Variables Monotone Discriminant Function Method for CLASS Variables FCS Method for Continuous Variables FCS Method for CLASS Variables FCS Method with Trace Plot MCMC Method Producing Monotone Missingness with MCMC Checking Convergence in MCMC Saving and Using Parameters for MCMC Transforming to Normality Multistage Imputation
- References
| Multiple Imputation Efficiency |
The relative efficiency (RE) of using the finite 
imputation estimator, rather than using an infinite number for the fully efficient imputation, in units of variance, is approximately a function of and 
(Rubin 1987, p. 114):
 |
Table 56.7 shows relative efficiencies with different values of and .
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10% |
20% |
30% |
50% |
70% |
3 |
0.9677 |
0.9375 |
0.9091 |
0.8571 |
0.8108 |
5 |
0.9804 |
0.9615 |
0.9434 |
0.9091 |
0.8772 |
10 |
0.9901 |
0.9804 |
0.9709 |
0.9524 |
0.9346 |
20 |
0.9950 |
0.9901 |
0.9852 |
0.9756 |
0.9662 |
The table shows that for situations with little missing information, only a small number of imputations are necessary. In practice, the number of imputations needed can be informally verified by replicating sets of imputations and checking whether the estimates are stable between sets (Horton and Lipsitz 2001, p. 246).