The stalactite plot for the detection of multivariate outliers
β Scribed by A. C. Atkinson; H.-M. Mulira
- Book ID
- 104641389
- Publisher
- Springer US
- Year
- 1993
- Tongue
- English
- Weight
- 678 KB
- Volume
- 3
- Category
- Article
- ISSN
- 0960-3174
No coin nor oath required. For personal study only.
β¦ Synopsis
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.
π SIMILAR VOLUMES
Rohlf (1975, Biometrics 31, 93-101) proposed a method of detecting outliers in multivariate data by testing the largest edge of the minimum spanning tree. It is shown here that tests against the gamma distribution are extremely liberal. Furthermore, results depend on the correlation structure of the