ISSN:
1573-1375
Keywords:
elemental set
;
Mahalanobis distance
;
masking
;
Monte Carlo testing
;
normalized distance
;
outliers
;
simulation
;
stalactite plot
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
,
Mathematics
Notes:
Abstract Detection of multiple outliers in multivariate data using Mahalanobis distances requires robust estimates of the means and covariance of the data. We obtain this by sequential construction of an outlier free subset of the data, starting from a small random subset. The stalactite plot provides a cogent summary of suspected outliers as the subset size increases. The dependence on subset size can be virtually removed by a simulation-based normalization. Combined with probability plots and resampling procedures, the stalactite plot, particularly in its normalized form, leads to identification of multivariate outliers, even in the presence of appreciable masking.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00146951
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