The minimum covariance determinant (MCD) scatter estimator is a highly robust estimator for the dispersion matrix of a multivariate, elliptically symmetric distribution. It is relatively fast to compute and intuitively appealing. In this note we derive its influence function and compute the asymptotic variances of its elements. A comparison with the one step reweighted MCD and with S-estimators is made. Also finite-sample results are reported. © 1999 Academic Press.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
The minimum covariance determinant (MCD) method of Rousseeuw (1984) is a highly robust estimator of ...
In this paper, the influence functions and limiting distributions of the canonical correlations and ...
AbstractIn modern statistics the robust estimation of parameters is a central problem, i.e., an esti...
The minimum covariance determinant (MCD) scatter estimator is a highly robust estimator for the disp...
The minimum covariance determinant (MCD) scatter estimator is a highly robust estimator for the disp...
The minimum covariance determinant (MCD) estimators of multivariate location and scatter are robust ...
We define the minimum covariance determinant functionals for multivariate location and scatter throu...
The minimum covariance determinant (MCD) estimator of scatter is one of the most famous robust proce...
In Cator and Lopuhaä (arXiv:math.ST/0907.0079) [3], an asymptotic expansion for the minimum covarian...
In this paper, the influence functions and limiting distributions of the canonical correlations and ...
AbstractIn Cator and Lopuhaä (arXiv:math.ST/0907.0079) [3], an asymptotic expansion for the minimum ...
AbstractIn this paper, the influence functions and limiting distributions of the canonical correlati...
Visuri, Koivunen and Oja (2003) proposed and illustrated the use of the affine equivariant rank cova...
Vis uri et al. (20Gl) proposed and illustrated the use ofthe affine equivariant rank covariance matr...
The minimum covariance determinant (MCD) approach estimates the location and scatter matrix using th...
The minimum covariance determinant (MCD) method of Rousseeuw (1984) is a highly robust estimator of ...
In this paper, the influence functions and limiting distributions of the canonical correlations and ...
AbstractIn modern statistics the robust estimation of parameters is a central problem, i.e., an esti...
The minimum covariance determinant (MCD) scatter estimator is a highly robust estimator for the disp...
The minimum covariance determinant (MCD) scatter estimator is a highly robust estimator for the disp...
The minimum covariance determinant (MCD) estimators of multivariate location and scatter are robust ...
We define the minimum covariance determinant functionals for multivariate location and scatter throu...
The minimum covariance determinant (MCD) estimator of scatter is one of the most famous robust proce...
In Cator and Lopuhaä (arXiv:math.ST/0907.0079) [3], an asymptotic expansion for the minimum covarian...
In this paper, the influence functions and limiting distributions of the canonical correlations and ...
AbstractIn Cator and Lopuhaä (arXiv:math.ST/0907.0079) [3], an asymptotic expansion for the minimum ...
AbstractIn this paper, the influence functions and limiting distributions of the canonical correlati...
Visuri, Koivunen and Oja (2003) proposed and illustrated the use of the affine equivariant rank cova...
Vis uri et al. (20Gl) proposed and illustrated the use ofthe affine equivariant rank covariance matr...
The minimum covariance determinant (MCD) approach estimates the location and scatter matrix using th...
The minimum covariance determinant (MCD) method of Rousseeuw (1984) is a highly robust estimator of ...
In this paper, the influence functions and limiting distributions of the canonical correlations and ...
AbstractIn modern statistics the robust estimation of parameters is a central problem, i.e., an esti...