Publication: Combining random and specific directions for outlier detection and robust estimation in high-dimensional multivariate data
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2007
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Taylor & Francis
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Abstract
A powerful procedure for outlier detection and robust estimation of shape and location
with multivariate data in high dimension is proposed. The procedure searches for
outliers in univariate projections on directions that are obtained both randomly, as in the
Stahel-Donoho method, and by maximizing and minimizing the kurtosis coefficient of
the projected data, as in the Pe˜na and Prieto method.We propose modifications of both
methods to improve their computational efficiency and combine them in a procedure
which is affine equivariant, has a high breakdown point, is fast to compute and can be
applied when the dimension is large. Its performance is illustrated with a Monte Carlo
experiment and in a real dataset.
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Kurtosis, Projections, Stahel-Donoho
Bibliographic citation
Journal of Computational and Graphical Statistics, 2007, v. 16, n. 1, p. 228-254