The minimum covariance determinant (MCD) method is a highly robust estimator of
multivariate location and scatter, for which a fast algorithm is available. Since estimating the …

The minimum covariance determinant (MCD) estimator is a highly robust estimator of
multivariate location and scatter. It can be computed efficiently with the FAST‐MCD …

Cronbach's alpha is a popular method to measure reliability, eg in quantifying the reliability
of a score to summarize the information of several items in questionnaires. The alpha …

A robust principal component analysis can be easily performed by computing the
eigenvalues and eigenvectors of a robust estimator of the covariance or correlation matrix. In …

When applying a statistical method in practice it often occurs that some observations deviate
from the usual assumptions. However, many classical methods are sensitive to outliers. The …

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 …

The minimum volume ellipsoid (MVE) estimator is based on the smallest volume ellipsoid
that covers h of the n observations. It is an affine equivariant, high‐breakdown robust …

We use the forward search to provide robust Mahalanobis distances to detect the presence
of outliers in a sample of multivariate normal data. Theoretical results on order statistics and …

A general method for exploring multivariate data by comparing different estimates of
multivariate scatter is presented. The method is based on the eigenvalue–eigenvector …

We introduce a robust method for multivariate regression based on robust estimation of the
joint location and scatter matrix of the explanatory and response variables. As a robust …