Robust Automatic Methods for Outlier and Error Detection

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Description/Abstract

Editing in surveys of economic populations is often complicated by the fact that
outliers due to errors in the data are mixed in with correct, but extreme, data values. In
this paper we describe and evaluate two automatic techniques for error identification
in such long tailed data distributions. The first is a forward search procedure based on
finding a sequence of error-free subsets of the error contaminated data and then using
regression modelling within these subsets to identify errors. The second uses a robust
regression tree modelling procedure to identify errors. Both approaches can be
implemented on a univariate basis or on a multivariate basis. An application to a
business survey data set that contains a mix of extreme errors and true outliers is
described.