An Evaluation of Some Robust Estimators of Regression Coefficients
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Abstract
In the theory of regression analysis, the method of least squares is most commonly used because of its mathematical beauty and computational simplicity. However, this method is now criticized more and more because it often has very poor performance when there are outliers in the data. In this connection a variety of robust statistics are developed for that they are not unduly a ected by outliers. In this thesis comparison studies have been made for several robust statistics to see which performs better than the others. Monte Carlo simulation has been used to carry out the comparison of these statistics, including the least absolute deviations estimator and the least median of squares estimator, least trimmed squares estimator and some M-estimators.