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Finding an unknown number of multivariate outliers
Author(s) -
Riani Marco,
Atkinson Anthony C.,
Cerioli Andrea
Publication year - 2009
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2008.00692.x
Subject(s) - mahalanobis distance , outlier , multivariate statistics , statistics , statistic , mathematics , test statistic , robust statistics , multivariate normal distribution , statistical hypothesis testing , computer science , pattern recognition (psychology) , artificial intelligence
Summary. We use the forward search to provide robust Mahalanobis distances to detect the presence of outliers in a sample of multivariate normal data. Theoretical results on order statistics and on estimation in truncated samples provide the distribution of our test statistic. We also introduce several new robust distances with associated distributional results. Comparisons of our procedure with tests using other robust Mahalanobis distances show the good size and high power of our procedure. We also provide a unification of results on correction factors for estimation from truncated samples.