Least absolute error regression

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dc.contributor.authorTung, Yi-Yuanen_US
dc.date.accessioned2024-08-15T20:10:35Z
dc.date.available2024-08-15T20:10:35Z
dc.date.copyright1998en_US
dc.date.issued1998
dc.degree.departmentDepartment of Mathematics and Statisticsen_US
dc.degree.levelMaster of Science M.Sc.en
dc.description.abstractRegression analysis is usually based on the method of least squares . However, the least squares estimator is highly sensitive to outliers and can be far from optimal when the errors are non-normal in distribution. As an alternative to the least squares estimator, the least absolute error estimator has attracted much attention during the past thirty years with the development of several efficient algorithms based on its connections to linear programming, least absolute error regression can be put into practical use. In this thesis, we review some basic theoretical properties of least absolute error regression, examine the asymptotic efficiency of the least absolute error estimator relative to the least squares estimator for a general family of error distribution, and demonstrate its use for practical data analysis in a variety of situations using computer routines written in S-Plus .
dc.format.extent89 pages
dc.identifier.urihttps://hdl.handle.net/1828/19946
dc.rightsAvailable to the World Wide Weben_US
dc.titleLeast absolute error regressionen_US
dc.typeThesisen_US

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