Robust estimation using the Huber function with a data-dependent tuning constant

Wang, Y-G., Lin, X., Zhu, Min, & Bai, Z. D. (2007) Robust estimation using the Huber function with a data-dependent tuning constant. Journal of Computational and Graphical Statistics, 16(2), pp. 468-481.

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Robust estimation often relies on a dispersion function that is more slowly varying at large values than the square function. However, the choice of tuning constant in dispersion functions may impact the estimation efficiency to a great extent. For a given family of dispersion functions such as the Huber family, we suggest obtaining the "best" tuning constant from the data so that the asymptotic efficiency is maximized. This data-driven approach can automatically adjust the value of the tuning constant to provide the necessary resistance against outliers. Simulation studies show that substantial efficiency can be gained by this data-dependent approach compared with the traditional approach in which the tuning constant is fixed. We briefly illustrate the proposed method using two datasets.

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18 citations in Scopus
14 citations in Web of Science®
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ID Code: 90494
Item Type: Journal Article
Refereed: Yes
Additional Information: ISI Document Delivery No.: 176AY
Times Cited: 9
Cited Reference Count: 17
Wang, You-Gan Lin, Xu Zhu, Min Bai, Zhidong
Amer statistical assoc
Keywords: asymptotic efficiency, M-estimation, robust estimation, regression, criterion, location, squares
DOI: 10.1198/106186007x180156
ISSN: 1061-8600
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Current > Schools > School of Economics & Finance
Copyright Owner: Copyright 2007 American Statistical Association, Institute of Mathematical Statisticsand Interface Foundation of North America
Deposited On: 18 Nov 2015 03:46
Last Modified: 27 Sep 2016 01:33

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