Imputation of missing data with different missingness mechanism

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...

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Bibliographic Details
Main Authors: Ho, Ming Kang (Author), Yusof, Fadhilah (Author), Mohamad, Ismail (Author)
Format: Article
Language:English
Published: Penerbit UTM Press, 2012.
Subjects:
Online Access:Get fulltext
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100 1 0 |a Ho, Ming Kang  |e author 
700 1 0 |a Yusof, Fadhilah  |e author 
700 1 0 |a Mohamad, Ismail  |e author 
245 0 0 |a Imputation of missing data with different missingness mechanism 
260 |b Penerbit UTM Press,   |c 2012. 
856 |z Get fulltext  |u http://eprints.utm.my/id/eprint/47093/1/HoMingKang2012_ImputationofMissingData.pdf 
520 |a 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. 
546 |a en 
650 0 4 |a TK Electrical engineering. Electronics Nuclear engineering