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http://dx.doi.org/10.5351/KJAS.2012.25.1.205

Robust Response Transformation Using Outlier Detection in Regression Model  

Seo, Han-Son (Department of Applied Statistics, Konkuk University)
Lee, Ga-Yoen (Strategy & Planning Team, Okcashbag Service)
Yoon, Min (Department of Statistics, Pukyong National University)
Publication Information
The Korean Journal of Applied Statistics / v.25, no.1, 2012 , pp. 205-213 More about this Journal
Abstract
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.
Keywords
Box-Cox transformation; variable transformation; outlier; least trimmed squares estimator; regression model;
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