선형판별분석에서 MCMC다중대체법의 효율에 관한 연구 (A Study on the efficiency of the MCMC multiple imputation In LDA)

서지정보

· 발행기관 : 대한안전경영과학회
· 수록지 정보 : 대한안전경영과학회지 / 11권 / 3호 / 189 ~ 198페이지
· 저자명 : 유희경, 김명철

초록

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.

영어초록

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.

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