Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
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고차원 자료에서 영향점의 영향을 평가하기 위한 그래픽 방법

Graphical method for evaluating the impact of influential observations in high-dimensional data

  • Ahn, Sojin (Department of Statistics, Pukyong National University) ;
  • Lee, Jae Eun (Department of Statistics, Pukyong National University) ;
  • Jang, Dae-Heung (Department of Statistics, Pukyong National University)
  • 투고 : 2017.11.03
  • 심사 : 2017.11.23
  • 발행 : 2017.11.30
⟨ 이전 논문 다음 논문 ⟩

초록

고차원 자료에서는 관측값의 개수보다 변수의 개수가 과다하게 많은 것이 특징이다. 그러므로 회귀 계수 추정에 있어 관측값의 영향이 매우 클 수 있다. Jang과 Anserson-Cook (2017)은 라쏘추정량 사용시 영향점의 영향을 평가할 수 있는 라쏘 영향그림을 제안하였다. 본 연구에서는 고차원 자료에서 영향점을 평가하기 위한 그래픽 방법들로서 라쏘 영향그림 뿐만 아니라 라쏘 변수선택 순위그림, 삼차원 라쏘 영향그림을 제안하였다. 실세 두 가지 고차원 자료 예들에 영향점들을 찾기 위한 회귀진단 수단으로서 세가지 그래픽 방법들을 사용하여 본 결과 영향점들을 효과적으로 찾아낼 수 있었다.

In the high-dimensional data, the number of variables is very larger than the number of observations. In this case, the impact of influential observations on regression coefficient estimates can be very large. Jang and Anderson-Cook (2017) suggested the LASSO influence plot. In this paper, we propose the LASSO influence plot, LASSO variable selection ranking plot, and three-dimensional LASSO influence plot as graphical methods for evaluating the impact of influential observations in high-dimensional data. With real two high-dimensional data examples, we apply these graphical methods as the regression diagnostics tools for finding influential observations. It has been found that we can obtain influential observations with by these graphical methods.

키워드

과제정보

연구 과제 주관 기관 : 부경대학교

참고문헌

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