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Robust Automatic Methods for Outlier and Error Detection

Chambers, Ray, Hentges, Adão and Zhao, Xinqiang (2003) Robust Automatic Methods for Outlier and Error Detection , Southampton, UK Southampton Statistical Sciences Research Institute 29pp. (S3RI Methodology Working Papers, M03/17).

Record type: Monograph (Project Report)

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.

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Published date: 2003

Identifiers

Local EPrints ID: 8167
URI: http://eprints.soton.ac.uk/id/eprint/8167
PURE UUID: 4d17cb45-a9c1-4860-a3a4-4c1414319735

Catalogue record

Date deposited: 11 Jul 2004
Last modified: 17 Jul 2017 17:13

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Contributors

Author: Ray Chambers
Author: Adão Hentges
Author: Xinqiang Zhao

University divisions

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