The MI Procedure
- Overview
- Getting Started
-
Syntax
-
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
-
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
| Monotone Methods for Data Sets with Monotone Missing Patterns |
For data sets with monotone missing data patterns, you can use monotone methods to impute missing values for the variables. A monotone method creates multiple imputations by imputing missing values sequentially over the variables taken one at a time.
For example, with variables 
, 
, ..., 
(in that order) in the VAR statement, a monotone method sequentially simulates a draw for missing values for variables , ..., . That is, the missing values are imputed by using the sequence
 |
 |
 |
|||
 |
|
 |
|||
 |
 |
|
|||
|
|
|
|||
 |
|
 |
|||
 |
|
 |
where 
is the set of observed 
values, 
is the set of simulated parameters for the conditional distribution of 
given covariates constructed from variables , , ..., 
, and 
is the set of imputed values.
The missing values for the leading variable are not imputed, and missing values for , ..., are not imputed for those observations with missing values. For each subsequent variable with missing values, the corresponding imputation method is used to fit a model with covariates constructed from its preceding variables 
. The observed observations for , which include only observations with observed values for , are used in the model fitting. With this resulting model, a new model is drawn and then used to impute missing values for .
You can specify a separate monotone method for each imputed variable. If a method is not specified for the variable, then the default method is used. That is, a regression method is used for a continuous variable and a discriminant function method is used for a classification variable. For each imputed variable, you can also specify a set of covariates that are constructed from its preceding variables. If a set of covariates is not specified for the variable, all preceding variables in the VAR list are used as covariates.
You can use a regression method, a predictive mean matching method, or a propensity score method to impute missing values for a continuous variable; a logistic regression method for a classification variable with a binary or ordinal response; and a discriminant function method for a classification variable with a binary or nominal response. See the sections Monotone and FCS Regression Methods, Monotone and FCS Predictive Mean Matching Methods, Monotone Propensity Score Method, Monotone and FCS Discriminant Function Methods, and Monotone and FCS Logistic Regression Methods for these methods.