A simulation study of the robustness of the least median of squares estimator of slope in a regression through the origin model

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dc.contributor.author Paranagama, Thilanka Dilruwani
dc.date.accessioned 2010-12-17T22:01:18Z
dc.date.available 2010-12-17T22:01:18Z
dc.date.issued 2010-12-17
dc.identifier.uri http://hdl.handle.net/2097/7045
dc.description.abstract The principle of least squares applied to regression models estimates parameters by minimizing the mean of squared residuals. Least squares estimators are optimal under normality but can perform poorly in the presence of outliers. This well known lack of robustness motivated the development of alternatives, such as least median of squares estimators obtained by minimizing the median of squared residuals. This report uses simulation to examine and compare the robustness of least median of squares estimators and least squares estimators of the slope of a regression line through the origin in terms of bias and mean squared error in a variety of conditions containing outliers created by using mixtures of normal and heavy tailed distributions. It is found that least median of squares estimation is almost as good as least squares estimation under normality and can be much better in the presence of outliers. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Least median of squares estimates en_US
dc.subject Regression en_US
dc.subject Estimates en_US
dc.subject Median en_US
dc.subject Regression through the origin en_US
dc.title A simulation study of the robustness of the least median of squares estimator of slope in a regression through the origin model en_US
dc.type Report en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Statistics en_US
dc.description.advisor Paul I. Nelson en_US
dc.subject.umi Statistics (0463) en_US
dc.date.published 2010 en_US
dc.date.graduationmonth December en_US


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