Title
Fast and robust bootstrap for LTS
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Faculty of Social Sciences. Communication Sciences
Publication type
article
Publication
Amsterdam ,
Subject
Mathematics
Computer. Automation
Source (journal)
Computational statistics and data analysis. - Amsterdam
Volume/pages
48(2005) :4 , p. 703-715
ISSN
0167-9473
ISI
000226659000004
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
The least trimmed squares (LTS) estimator is a frequently used robust estimator of regression. When it comes to inference for the parameters of the regression model, the asymptotic normality of the LTS estimator can be used. However, this is usually not appropriate in situations where the use of robust estimators is recommended. The bootstrap method constitutes an alternative, but has two major drawbacks. First, since the LTS in itself is a computer-intensive estimator, the classical bootstrap can be extremely time-consuming. And second, the breakdown point of the procedure is lower than that of the estimator itself. To overcome these problems, an alternative bootstrap method is proposed which is both computationally simple and robust. In each bootstrap sample, instead of recalculating the LTS estimates, an approximation is computed using information from the LTS solution in the original sample. A simulation study shows that this method performs well, particularly regarding confidence intervals for the regression parameters. An example is given to illustrate the benefits of the method. (C) 2004 Elsevier B.V. All rights reserved.
E-info
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