Small sample corrections for LTS and MCD | Zendy

Greet Pison | Zendy; Stefan Van Aelst | Zendy; Gert Willems | Zendy

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Small sample corrections for LTS and MCD

Author(s) -

Greet Pison,

Stefan Van Aelst,

Gert Willems

Publication year - 2002

Publication title -

metrika

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.728

H-Index - 40

eISSN - 1435-926X

pISSN - 0026-1335

DOI - 10.1007/s001840200191

Subject(s) - estimator , mathematics , consistency (knowledge bases) , statistics , minimum variance unbiased estimator , covariance , consistent estimator , sample (material) , sample size determination , bias of an estimator , stein's unbiased risk estimate , strong consistency , least squares function approximation , discrete mathematics , chemistry , chromatography

The least trimmed squares estimator and the minimum covari-ance determinant estimator [5] are frequently used robust estimators of re-gression and of location and scatter. Consistency factors can be computedfor both methods to make the estimators consistent at the normal model.However, for small data sets these factors do not make the estimator un-biased. Based on simulation studies we therefore construct formulas whichallow us to compute small sample correction factors for all sample sizes anddimensions without having to carry out any new simulations. We give someexamples to illustrate the effect of the correction factor.status: publishe

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