JSM 2004 Online Program

Abstract #301929

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Activity Number:	306
Type:	Contributed
Date/Time:	Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
Sponsor:	Section on Nonparametric Statistics
Abstract - #301929
Title:	Robust Weighted LAD Regression
Author(s):	Avi H. Giloni*+ and Jeffrey S. Simonoff and Bhaskar Sengupta
Companies:	Yeshiva University and New York University and Yeshiva University
Address:	Sy Syms School of Business, New York, NY, 10033,
Keywords:	LAD regression ; breakdown point ; robustness ; high breakdown estimator
Abstract:	Consider a regression problem with a given design matrix, where there is possible contamination in the response variable values. We show that a weighted version of the least absolute deviation (LAD) regression estimator can attain a high breakdown point given the design (as high as 30%). This can be accomplished even in the presence of designs with "leverage" points. Furthermore, we show that the estimator is asymptotically normal, root n consistent, and give the asymptotic variance of the estimator. Several examples illustrating the usefulness of the estimator are provided.

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