The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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An Outlier Detection Method in Penalized Spline Regression Models

벌점 스플라인 회귀모형에서의 이상치 탐지방법

  • Seo, Han Son (Department of Applied Statistics, Konkuk University) ;
  • Song, Ji Eun (Department of Applied Statistics, Konkuk University) ;
  • Yoon, Min (Department of Statistics, Pukyong National University)
  • 서한손 (건국대학교 응용통계학과) ;
  • 송지은 (건국대학교 응용통계학과) ;
  • 윤민 (부경대학교 통계학과)
  • Received : 2013.07.15
  • Accepted : 2013.08.21
  • Published : 2013.08.31
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Abstract

The detection and the examination of outliers are important parts of data analysis because some outliers in the data may have a detrimental effect on statistical analysis. Outlier detection methods have been discussed by many authors. In this article, we propose to apply Hadi and Simonoff's (1993) method to penalized spline a regression model to detect multiple outliers. Simulated data sets and real data sets are used to illustrate and compare the proposed procedure to a penalized spline regression and a robust penalized spline regression.

이상치가 존재하는 경우 모형 적합의 결과가 왜곡될 수 있기 때문에 이상치 탐색은 데이터분석에 있어서 매우 중요하다. 이상치 탐지 방법은 많은 학자들에 의해 연구되어 왔다. 본 논문에서는 Hadi와 Simonoff (1993)가 제안한 직접적 이상치 탐지 방법을 벌점 스플라인 회귀모형에 적용하여 이상치를 탐지하는 과정을 제안하며 모의실험과 실제 데이터에 적용을 통하여 스플라인 회귀모형, 강건 벌점 스플라인 회귀모형과 효율성을 비교한다.

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References

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