Earticle

초록

영어

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.

목차

Abstract
 1. 서론
 2. 본론
  2.1 판별규칙(Classification rule)
  2.2 결측치 대체 방법
 3. 시뮬레이션 비교연구
 4. 결론
 5. 참고문헌

키워드

저자

• 유희경 [ Hee Kyung Yoo | 강원대학교 삼척캠퍼스 컴퓨터공학과 ] 교신저자
• 김명철 [ Myung Cheol Kim | 강원대학교 삼척캠퍼스 산업경영공학과 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  대한안전경영과학회지 [Journal of Korea Safety Management & Science]

• 간기

  계간

• pISSN

  1229-6783

• eISSN

  2288-1484

• 수록기간

  1999~2025

• 십진분류

  KDC 530 DDC 620