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Robust Response Transformation Using Outlier Detection in Regression Model
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 Title & Authors
Robust Response Transformation Using Outlier Detection in Regression Model
Seo, Han-Son; Lee, Ga-Yoen; Yoon, Min;
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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;
 Cited by
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