Robustness against unexpected dependence in the location model
Robustness of M-estimates of location against unexpected dependence in the data is studied via a min--max asymptotic variance approach. A measure of dependence is defined and used to construct a neighborhood of the classical location model which includes dependent observations. The solution of the min--max problem is a Huber's type M-estimate with psi-function [psi]c. The tuning constant c tends to zero, i.e. [psi]c(x) --> sign(x) (the sample median score function), when the maximum degree of dependence allowed in the neighborhood increases. Thus the median, which is the most bias-robust estimate of location, is also approximately the most variance-robust in the present context.
Year of publication: |
1990
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Authors: | Zamar, Ruben H. |
Published in: |
Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 9.1990, 4, p. 367-374
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Publisher: |
Elsevier |
Subject: | Robustness M-estimates dependence |
Saved in:
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