Robust Response Transformation Using Outlier Detection in Regression Model
회귀모형에서 이상치 검색을 이용한 로버스트 변수변환방법

Han-Son Seo, Ga-Yoen Lee, Min Yoon
2012 Korean Journal of Applied Statistics  
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
more » ... 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.
doi:10.5351/kjas.2012.25.1.205 fatcat:ue4gr3ycmncmxpq2ucpww3d5nu