Skip to main content

Robust automatic methods for outlier and error detection

Buy Article:

$51.00 plus tax (Refund Policy)

Abstract:

Summary. 

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. We describe and evaluate two automatic techniques for the identification of errors 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.

Keywords: Gross errors; M-estimates; Regression tree model; Representative outliers; Robust regression; Survey data editing

Document Type: Research Article

DOI: https://doi.org/10.1111/j.1467-985X.2004.00748.x

Affiliations: 1: University of Southampton, UK. 2: Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.

Publication date: 2004-05-01

  • Access Key
  • Free ContentFree content
  • Partial Free ContentPartial Free content
  • New ContentNew content
  • Open Access ContentOpen access content
  • Partial Open Access ContentPartial Open access content
  • Subscribed ContentSubscribed content
  • Partial Subscribed ContentPartial Subscribed content
  • Free Trial ContentFree trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more