Asymptotic expansion of the minimum covariance determinant estimators

  title={Asymptotic expansion of the minimum covariance determinant estimators},
  author={Eric A. Cator and Hendrik P. Lopuha{\"a}},
  journal={J. Multivariate Analysis},
In Cator and Lopuhaä [3] an asymptotic expansion for the MCD estimators is established in a very general framework. This expansion requires the existence and non-singularity of the derivative in a first-order Taylor expansion. In this paper, we prove the existence of this derivative for multivariate distributions that have a density and provide an explicit expression. Moreover, under suitable symmetry conditions on the density, we show that this derivative is non-singular. These symmetry… CONTINUE READING

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Showing 1-10 of 19 references

Influence function and efficiency of the minimum covariance determinant scatter matrix estimator

C. Croux, G. Haesbroeck
J. Multivariate Anal • 1999
View 5 Excerpts
Highly Influenced

Asymptotics for the minimum covariance determinant estimator

R. W. Butler, P. L. Davies, M. Jhun
Ann. Statist. 21, • 1993
View 8 Excerpts
Highly Influenced

Robust dimension reduction based on canonical correlation

J. Multivariate Analysis • 2009
View 1 Excerpt

Influence functions and efficiencies of the canonical correlation and vector estimates based on scatter and shape matrices

S Taskinen, C. Croux, A. Kankainen, E. Ollila, H. Oja
J. Multivariate Anal • 2006
View 1 Excerpt

Robust weighted orthogonal regression in the errorsin-variables model

M. Fekri, A. Ruiz-Gazen
J. Multivariate Anal • 2004
View 1 Excerpt

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