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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/26322
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| Title: | Robust Weighted LAD Regression |
| Authors: | Giloni, Avi
Simonoff, Jeffrey S.
Sengupta, Bhaskar |
| Keywords: | Breakdown point
Leverage points
Outliers
Robust regression |
| Issue Date: | 25-Feb-2005 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | SOR-2004-5 |
| Abstract: | The least squares linear regression estimator is well-known to be highly
sensitive to unusual observations in the data, and as a result many more
robust estimators have been proposed as alternatives. One of the
earliest proposals was least-sum of absolute deviations (LAD)
regression, where the regression coefficients are estimated through
minimization of the sum of the absolute values of the residuals. LAD
regression has been largely ignored as a robust alternative to least
squares, since it can be strongly affected by a single observation (that
is, it has a breakdown point of 1/n, where n is the sample size). In
this paper we show that judicious choice of weights can result in a
weighted LAD estimator with much higher breakdown point. We discuss the
properties of the weighted LAD estimator, and show via simulation that
its performance is competitive with that of high breakdown regression
estimators, particularly in the presence of outliers located at leverage
points. We also apply the estimator to several real data sets. |
| URI: | http://hdl.handle.net/2451/26322 |
| Appears in Collections: | IOMS: Statistics Working Papers
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