Small Sample Corrections for LTS and MCD

G. Pison (), S. Van Aelst () and G. Willems ()
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G. Pison: Universitaire Instelling Antwerpen (UIA), Department of Mathematics and Computer Science
S. Van Aelst: Universitaire Instelling Antwerpen (UIA), Department of Mathematics and Computer Science
G. Willems: Universitaire Instelling Antwerpen (UIA), Department of Mathematics and Computer Science

A chapter in Developments in Robust Statistics, 2003, pp 330-343 from Springer

Abstract: Summary The least trimmed squares estimator and the minimum covariance determinant estimator Rousseeuw (1984) are frequently used robust estimators of regression and of location and scatter. Consistency factors can be computed for both methods to make the estimators consistent at the normal model. However, for small data sets these factors do not make the estimator unbiased. Based on simulation studies we therefore construct formulas which allow us to compute small sample correction factors for all sample sizes and dimensions without having to carry out any new simulations. We give some examples to illustrate the effect of the correction factor.

Keywords: Robustness; Least Trimmed Squares estimator; Minimum Covariance Determinant estimator; Bias (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1007/978-3-642-57338-5_29

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