  Most multivariate outlier detection procedures ignore the spatial dependency
of observations, which is present in many real data sets from various
application areas. This paper introduces a new outlier detection method that
accounts for a (continuously) varying covariance structure, depending on the
spatial neighborhood of the observations. The underlying estimator thus
constitutes a compromise between a unified global covariance estimation, and
local covariances estimated for individual neighborhoods. Theoretical
properties of the estimator are presented, in particular related to robustness
properties, and an efficient algorithm for its computation is introduced. The
performance of the method is evaluated and compared based on simulated data and
for a data set recorded from Austrian weather stations.
