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   2011, Vol. 19 Issue (7) : 1083-1090     DOI:
研究方法 |
Handling Missing Data: Expectation-Maximization Algorithm and Markov Chain Monte Carlo Algorithm
MU Shou-Kuan;ZHOU Wei
Department of Education, Zhangzhou Normal University, Zhangzhou 363000, China
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Abstract  Dataset with missing data is quite common in psychological research, which usually creates major problems in statistical inference. Maximum Likelihood Estimator and Multiple Imputation based on Bayesian Estimator are most important methods of handling missing data. Expectation-Maximization Algorithm, included in Maximum Likelihood Estimator is quite advantageous to flexible use and accurate results, while Markov Chain Monte Carlo Algorithm may achieve multiple imputation more easily and can be applied to handling missing data in complex situations. Finally, suitable statistical software is discussed in the field of psychological study.
Keywords missing data      Expectation-Maximization algorithm      Markov Chain Monte Carlo algorithm      Maximum Likelihood Estimator      multiple imputation     
Corresponding Authors: MU Shou-Kuan;ZHOU Wei   
Issue Date: 15 July 2011
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MU Shou-Kuan,ZHOU Wei. Handling Missing Data: Expectation-Maximization Algorithm and Markov Chain Monte Carlo Algorithm[J]. , 2011, 19(7): 1083-1090.
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http://journal.psych.ac.cn/xlkxjz/EN/     OR     http://journal.psych.ac.cn/xlkxjz/EN/Y2011/V19/I7/1083
[1] WANG Mengcheng;YE Haosheng. Planned Missing Data Design: Through Intended Missing Data Make Research More Effective[J]. Advances in Psychological Science, 2014, 22(6): 1025-1035.
[2] YE Sujing; TANG Wenqing; ZHANG Minqiang; CAO Weicong. Techniques for Missing Data in Longitudinal Studies and Its Application[J]. Advances in Psychological Science, 2014, 22(12): 1985-1994.
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