The Comparison of Elementaπ Teachers' Longitudinal Advice Network Missing Data Analysis: Based on Multiple Imputation when Missing Not At idom(MNAR)

2019 ◽  
Vol 32 (4) ◽  
pp. 671-703
Author(s):  
Chong Min Kim
Keyword(s):  
Data Analysis ◽  
Missing Data ◽  
Multiple Imputation ◽  
Missing Data Analysis

Regression multiple imputation for missing data analysis

2020 ◽  
Vol 29 (9) ◽  
pp. 2647-2664
Author(s):  
Lili Yu ◽  
Liang Liu ◽  
Karl E Peace
Keyword(s):  
Data Analysis ◽  
Missing Data ◽  
Multiple Imputation ◽  
Expectation Maximization Algorithm ◽  
Real Data ◽  
Imputation Method ◽  
Missing Data Analysis ◽  
Multiple Imputation Method ◽  
Asymptotically Efficient ◽  
Parameter Estimators

Iterative multiple imputation is a popular technique for missing data analysis. It updates the parameter estimators iteratively using multiple imputation method. This technique is convenient and flexible. However, the parameter estimators do not converge point-wise and are not efficient for finite imputation size m. In this paper, we propose a regression multiple imputation method. It uses the parameter estimators obtained from multiple imputation method to estimate the parameter estimators based on expectation maximization algorithm. We show that the resulting estimators are asymptotically efficient and converge point-wise for small m values, when the iteration k of the iterative multiple imputation goes to infinity. We evaluate the performance of the new proposed methods through simulation studies. A real data analysis is also conducted to illustrate the new method.

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