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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/26335
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| Title: | The Conditional Breakdown Properties of Least Absolute Value Local
Polynomial Estimators |
| Authors: | Giloni, Avi
Simonoff, Jeffrey S. |
| Keywords: | Nonparametric regression
robust estimation |
| Issue Date: | 7-Dec-2003 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | SOR-2003-7 |
| Abstract: | Nonparametric regression techniques provide an effective way of
identifying and examining structure in regression data. The standard
approaches to nonparametric regression, such as local polynomial and
smoothing spline estimators, are sensitive to unusual observations, and
alternatives designed to be resistant to such observations have been
proposed as a solution. Unfortunately, there has been little examination
of the resistance properties of these proposed estimators. In this paper
we examine the breakdown properties of local polynomial estimation based
on least absolute values, rather than least squares. We show that the
breakdown at any evaluation point depends on the observed distribution
of observations and the kernel weight function used, and make
recommendations regarding choice of kernel based on two different
breakdown measures. The results suggest that the breakdown point at an
evaluation point provides a useful summary of the resistance of the
regression estimator to unusual observations. |
| URI: | http://hdl.handle.net/2451/26335 |
| Appears in Collections: | IOMS: Statistics Working Papers
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