A comparative analysis of multiple outlier detection procedures in the linear regression model

James W. Wisnowski, Douglas Montgomery, James R. Simpson

Research output: Contribution to journalArticle

47 Scopus citations


We evaluate several published techniques to detect multiple outliers in linear regression using an extensive Monte Carlo simulation. These procedures include both direct methods from algorithms and indirect methods from robust regression estimators. We evaluate the impact of outlier density and geometry, regressor variable dimension, and outlying distance in both leverage and residual on detection capability and false alarm (swamping) probability. The simulation scenarios focus on outlier configurations likely to be encountered in practice and use a designed experiment approach. The results for each scenario provide insight and limitations to performance for each technique. Finally, we summarize each procedure's performance and make recommendations.

Original languageEnglish (US)
Pages (from-to)351-382
Number of pages32
JournalComputational Statistics and Data Analysis
Issue number3
Publication statusPublished - May 28 2001



  • Minimum volume ellipsoid
  • Monte Carlo simulation
  • Multiple outliers
  • Outlier
  • Robust regression

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Statistics, Probability and Uncertainty
  • Electrical and Electronic Engineering
  • Computational Mathematics
  • Numerical Analysis
  • Statistics and Probability

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