Outlier detection by robust principal components analysis
Titel:
Outlier detection by robust principal components analysis
Auteur:
Caroni, C.
Verschenen in:
Communications in statistics
Paginering:
Jaargang 29 (2000) nr. 1 pagina's 139-151
Jaar:
2000
Inhoud:
The robust principal components analysis (RPCA) introduced by Campbell (Applied Statistics 1980, 29, 231-237) provides in addition to robust versions of the usual output of a principal components analysis, weights for the contribution of each point to the robust estimation of each component. Low weights may thus be used to indicate outliers. The present simulation study provides critical values for testing the kth smallest weight in the RPCA of a sample of n p-dimensional vectors, under the null hypothesis of a multivariate normal distribution. The cases p=2(2)10, 15, 20 for n=20, 30, 40, 50, 75, 100 subject to n≥p/2, are examined, with k≤√n.
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