Robust Response Transformation Using Outlier Detection in Regression Model

회귀모형에서 이상치 검색을 이용한 로버스트 변수변환방법

  • Received : 20111000
  • Accepted : 20111100
  • Published : 2012.02.29


Transforming response variable is a general tool to adapt data to a linear regression model. However, it is well known that response transformations in linear regression are very sensitive to one or a few outliers. Many methods have been suggested to develop transformations that will not be influenced by potential outliers. Recently Cheng (2005) suggested to using a trimmed likelihood estimator based on the idea of the least trimmed squares estimator(LTS). However, the method requires presetting the number of outliers and needs many computations. A new method is proposed, that can solve the problems addressed and improve the robustness of the estimates. The method uses a stepwise procedure, suggested by Hadi and Simonoff (1993), to detect outliers that determine response transformations.


Box-Cox transformation;variable transformation;outlier;least trimmed squares estimator;regression model


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