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High-Breakdown Regression by Least Quartile Differences [microform] / Eric Blankmeyer
Bib ID 5604886
Format BookBook, MicroformMicroform, OnlineOnline
Author
Blankmeyer, Eric
 
Online Versions
Description [S.l.] : Distributed by ERIC Clearinghouse, 1996 
16 p. 
Summary

A high-breakdown estimator is a robust statistic that can withstand a large amount of contaminated data. In linear regression, high-breakdown estimators can detect outliers and distinguish between good and bad leverage points. This paper summarizes the case for high-breakdown regression and emphasizes the least quartile difference estimator (LQD) proposed by C. Croux, P. J. Rousseeuw, and O. Hossjer (1994). This regression method examines the absolute differences between every pair of residuals and minimizes the first quartile of these differences with an adjustment for degrees of freedom. LQD is affine equivalent and has a 50% breakdown point, the highest possible. Its asymptotic efficiency is about 67% of least-squares, so LQD should be able to deal with anomalous observations and should also perform well when the data are not contaminated. Although interest in the approach is growing, software is still not widely available. An appendix presents a BASIC computer program for one type of high-breakdown regression. (Contains 17 references.) (SLD)

Notes

May also be available online. Address as at 14/8/18: https://eric.ed.gov/

Reproduction Microfiche. [Washington D.C.]: ERIC Clearinghouse microfiches : positive. 
Subjects Computer Software.  |  Estimation (Mathematics)  |  Least Squares Statistics.  |  Regression (Statistics)  |  Research Methodology.  |  Robustness (Statistics)  |  Outliers
Form/genre Reports, Evaluative.  |  Computer Programs.
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