Robust regression:a weighted least squares approach |
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Authors: | samprit Chatterjee Martin Mächler |
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Affiliation: | 1. Dept of Statistics &2. OR , New York University , 44 West 4th Street, Room 8-67, New York, 10012-1126, USA E-mail: schatter@stern.nyu.edu;3. Seminar für Statistik , Swiss Federal Inst. of Technology (ETH) , SWITZERLAND, Zurich, CH-8092 E-mail: maechler@stat.math.ethz.ch |
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Abstract: | Robust regression has not had a great impact on statistical practice, although all statisticians are convinced of its importance. The procedures for robust regression currently available are complex, and computer intensive. With a modification of the Gaussian paradigm, taking into consideration outliers and leverage points, we propose an iteratively weighted least squares method which gives robust fits. The procedure is illustrated by applying it on data sets which have been previously used to illustrate robust regression methods.It is hoped that this simple, effective and accessible method will find its use in statistical practice. |
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Keywords: | outliers leverage points influence M-estimator iterative reweighting masking |
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