Open AccessIMPUTATION OF MISSING DATA WITH DIFFERENT MISSINGNESS MECHANISM
Author(s) -
Ho Ming Kang,
Fadhilah Yusof,
Ismail Mohamad
Publication year - 2012
Publication title -
jurnal teknologi/jurnal teknologi
Language(s) - English
Resource type - Journals
eISSN - 2180-3722
pISSN - 0127-9696
DOI - 10.11113/jt.v57.1523
Subject(s) - missing data , imputation (statistics) , statistics , mean squared error , mathematics , expectation–maximization algorithm , standard error , mean absolute error , maximum likelihood
This paper presents a study on the estimation of missing data. Data samples with different missingness mechanism namely Missing Completely At Random (MCAR), Missing At Random (MAR) and Missing Not At Random (MNAR) are simulated accordingly. Expectation maximization (EM) algorithm and mean imputation (MI) are applied to these data sets and compared and the performances are evaluated by the mean absolute error (MAE) and root mean square error (RMSE). The results showed that EM is able to estimate the missing data with minimum errors compared to mean imputation (MI) for the three missingness mechanisms. However the graphical results showed that EM failed to estimate the missing values in the missing quadrants when the situation is MNAR.