The method proposed by Hadi (1994) for multiple outlier detection in a single group of multivariate data is adapted to the multiple cluster setting. The idea is to replace, in Hadi’s algorithm, the Gaussian distribution and the Mahalanobis distance with the K-component normal mixture model (with K > 1) and a coherent measure of discrepancy from a mixture distribution, respectively. The performance of the proposed procedure is illustrated on a real data set and compared, through a simulation study, with the method proposed by Caroni and Billor (2007) for detecting multiple outliers in grouped multivariate data.
Calò D. G. (2008). A Method for Oultier Detection in Grouped data. HEIDELBERG : Physica-Verlag.
A Method for Oultier Detection in Grouped data
CALO', DANIELA GIOVANNA
2008
Abstract
The method proposed by Hadi (1994) for multiple outlier detection in a single group of multivariate data is adapted to the multiple cluster setting. The idea is to replace, in Hadi’s algorithm, the Gaussian distribution and the Mahalanobis distance with the K-component normal mixture model (with K > 1) and a coherent measure of discrepancy from a mixture distribution, respectively. The performance of the proposed procedure is illustrated on a real data set and compared, through a simulation study, with the method proposed by Caroni and Billor (2007) for detecting multiple outliers in grouped multivariate data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.