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The MI Procedure
Overview
Getting Started
Syntax
PROC MI Statement
BY Statement
CLASS Statement
EM Statement
FCS Statement
FREQ Statement
MCMC Statement
MONOTONE Statement
TRANSFORM Statement
VAR Statement
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
Details: MI Procedure
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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