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 …

Cluster analysis refers to a wide range of data analytic techniques for class discovery and is
popular in many application fields. To assess the quality of a clustering result, different …

Interest in the skew-normal and related families of distributions has grown enormously over
recent years, as theory has advanced, challenges of data have grown, and computational …

The paper deals with the estimation of a high dimensional covariance with a conditional
sparsity structure and fast diverging eigenvalues. By assuming a sparse error covariance …

This book offers a new, fairly efficient, and robust alternative to analyzing multivariate data.
The analysis of data based on multivariate spatial signs and ranks proceeds very much as …

Clustering remains a vibrant area of research in statistics. Although there are many books on
this topic, there are relatively few that are well founded in the theoretical aspects. In Robust …

Independent component analysis (ICA) is a data analysis tool that can be seen as a
refinement of principal component analysis or factor analysis. ICA recovers the structures in …

Clustering Strategy and Method Selection Page 1 31 Clustering Strategy and Method
Selection Christian Hennig CONTENTS Abstract …

Equivariance and invariance issues arise as a fundamental but often problematic aspect of
multivariate statistical analysis. For multivariate quantile and related functions, we provide …

In independent component analysis it is assumed that the components of the observed
random vector are linear combinations of latent independent random variables, and the aim …