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A Study on the efficiency of the MCMC multiple imputation In LDA  

Yoo, Hee-Kyung (Dept. of computer engineering Kangwon National University)
Kim, Myung-Cheol (Dept. of Industrial & Management Engineering Kangwon National University)
Publication Information
Journal of the Korea Safety Management & Science / v.11, no.3, 2009 , pp. 189-198 More about this Journal
Abstract
This thesis studies two imputation methods, the MCMC method and the EM algorithm, that take care of the problem. The performance of the two methods for the linear (or quadratic) discriminant analysis are evaluated under various types of incomplete observations. Based on simulated experiments, the effect of the imputation using the EM algorithm and the MCMC method are evaluated and compared in terms of the probability of misclassification and the RMSE. This is done for the various cases of incomplete observations. The cases are differentiated by missing rates, sample sizes, and distances between two classification groups. The studies show that the probability of misclassification and the RMSE of the EM algorithm method is lower than the MCMC method. Therefore the imputation using the EM algorithm is more efficient than the MCMC method. And the probability of misclassification of the method that all vectors of observations with missing values are omitted from analysis is lower than the EM algorithm and the MCMC method when the samples size is small and the rate of missing values is extremely big.
Keywords
discriminant analysis; EM algorithm; MCMC;
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