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Robust linear regression

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Figure 2.1: Figure (a) shows the objective, ψ, and weight functions for the least-squares(top) and LAD (bottom) estimators
Figure 2.2: Least squares estimate (solid line) and M-estimate with Huber function (dashed line) for a dataset contains 20 observations two of which are high leverage outliers.
Table 3.1: Breakdown Points and Asymptotic Efficiencies of Various Regression Estimators Estimator Breakdown Point Bounded Asymptotic Efficiency
Figure 3.1: Plot of MSE of slope estimates vs. different cases for LMS, LTS, S, MM, and REWLS, for model 1 when n = 20.
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