Authors: | Bernholt, Thorsten |
Title: | Robust Estimators are Hard to Compute |
Language (ISO): | en |
Abstract: | In modern statistics, the robust estimation of parameters of a re- gression hyperplane is a central problem. Robustness means that the estimation is not or only slightly a®ected by outliers in the data. In this paper, it is shown that the following robust estimators are hard to compute: LMS, LQS, LTS, LTA, MCD, MVE, Constrained M es- timator, Projection Depth (PD) and Stahel-Donoho. In addition, a data set is presented such that the ltsReg-procedure of R has proba- bility less than 0.0001 of ¯nding a correct answer. Furthermore, it is described, how to design new robust estimators. |
Subject Headings: | algorithms complexity theory computational statistics robust statistics search heuristics |
URI: | http://hdl.handle.net/2003/22138 http://dx.doi.org/10.17877/DE290R-14253 |
Issue Date: | 2006-01-25T12:51:23Z |
Appears in Collections: | Sonderforschungsbereich (SFB) 475 |
Files in This Item:
File | Description | Size | Format | |
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tr52-05.pdf | DNB | 300.96 kB | Adobe PDF | View/Open |
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