Divergence measures for statistical data processing—An annotated bibliography -
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Normal mixture models are being increasingly used to model the distributions of a wide
variety of random phenomena and to cluster sets of continuous multivariate data. However …

In many ways, estimation by an appropriate minimum distance method is one of the most
natural ideas in statistics. However, there are many different ways of constructing an …

By starting from a natural class of robust estimators for generalized linear models based on
the notion of quasi-likelihood, we define robust deviances that can be used for stepwise …

Robust statistics is an extension of classical parametric statistics that specifically takes into
account the fact that the assumed parametric models used by the researchers are only …

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 EM algorithm is the standard tool for maximum likelihood estimation in finite mixture
models. The main drawbacks of the EM algorithm are its slow convergence and the …

Most outlier detection rules for multivariate data are based on the assumption of elliptical
symmetry of the underlying distribution. We propose an outlier detection method which does …

The generality and causes of Bergmann's rule have been debated vigorously in the last few
years, but Bergmann's clines are rarely explained in the context of life-history theory. We …

Robust Methods for Data Reduction gives a non-technical overview of robust data reduction
techniques, encouraging the use of these important and useful methods in practical …