응용통계연구 (The Korean Journal of Applied Statistics)

  • 제20권3호
  • /
  • Pages.551-559
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  • 2007
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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로지스틱회귀모형의 로버스트 추정을 위한 알고리즘

Algorithm for the Robust Estimation in Logistic Regression

  • 김부용 (숙명여자대학교 통계학과) ;
  • 강명욱 (숙명여자대학교 통계학과) ;
  • 최미애 (삼성전자 컴퓨터사업부)
  • Kim, Bu-Yong (Department of Statistics, Sookmyung Women's University) ;
  • Kahng, Myung-Wook (Department of Statistics, Sookmyung Women's University) ;
  • Choi, Mi-Ae (Computer Systems Division, Samsung Electronics Co.)
  • 발행 : 2007.11.30
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⟨ 이전 논문 다음 논문 ⟩

초록

로지스틱회귀에서 일반적으로 사용되는 최대우도추정법은 이상점에 대해 로버스트 하지 않다. 따라서 본 논문에서는 로지스틱회귀모형의 로버스트 추정을 위한 알고리즘을 제안하고자 한다. 이 알고리즘은 V-마스크 형태의 경계기준에 의해 나쁜 지렛점과 수직이상점을 식별하고, 식별 결과를 바탕으로 이상점의 영향력을 감소시키기 위한 효과적인 방안을 모색한다. 이상점의 영향력 감소는 가중치와 조정치를 적절히 선정함으로 가능하며, 그 결과 붕괴점이 높은 추정치를 얻게 된다. 제안된 알고리즘을 다양한 자료에 적용하여 정분류율을 측정하여 비교하였는데, 새로운 알고리즘이 최대우도추정보다 정확한 분류를 해 주는 것으로 평가되었다.

The maximum likelihood estimation is not robust against outliers in the logistic regression. Thus we propose an algorithm for the robust estimation, which identifies the bad leverage points and vertical outliers by the V-mask type criterion, and then strives to dampen the effect of outliers. Our main finding is that, by an appropriate selection of weights and factors, we could obtain the logistic estimates with high breakdown point. The proposed algorithm is evaluated by means of the correct classification rate on the basis of real-life and artificial data sets. The results indicate that the proposed algorithm is superior to the maximum likelihood estimation in terms of the classification.

키워드

참고문헌

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