Title:

Partial Mixture Estimation for Outlier Detection,
Mapping, and Clustering of Large Datasets


Abstract:

The covariance matrix is a key component of many
multivariate robust procedures, whether or not
the data are assumed to be Gaussian.  We examine
the idea of robustly fitting a mixture of multivariate
Gaussian densities, but when the number of components
is intentionally too few.  Using a minimum distance
criterion, we show how useful results may be
obtained in practice.  Application areas are numerous,
and examples will be provided.  We will reexamine
the classical Boston housing data, with spatial
views of the residuals, as well as similar California data.