Robust linear regression

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Show simple item record Bai, Xue 2012-11-21T19:25:29Z 2012-11-21T19:25:29Z 2012-11-21
dc.description.abstract In practice, when applying a statistical method it often occurs that some observations deviate from the usual model assumptions. Least-squares (LS) estimators are very sensitive to outliers. Even one single atypical value may have a large effect on the regression parameter estimates. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we review various robust regression methods including: M-estimate, LMS estimate, LTS estimate, S-estimate, [tau]-estimate, MM-estimate, GM-estimate, and REWLS estimate. Finally, we compare these robust estimates based on their robustness and efficiency through a simulation study. A real data set application is also provided to compare the robust estimates with traditional least squares estimator. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Linear regression model en_US
dc.subject Robust regression en_US
dc.title Robust linear regression en_US
dc.type Report en_US Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Statistics en_US
dc.description.advisor Weixin Yao en_US
dc.subject.umi Statistics (0463) en_US 2012 en_US December en_US

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