A biased-robust regression technique for the combined outlier-multicollinearity problem

James R. Simpson, Douglas Montgomery

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


The combined outlier-multicollinearity problem occurs frequently in regression data. Methods that successfully address this problem effectively combine biased and robust estimation techniques. A biased-robust estimator is proposed that uses a multi-stage generalized M-estimator with fully iterated ridge regression to successfully control both influence and collinearity in regression datasets. Two previously published approaches are compared with the proposal via simulation experiments. The best performing published technique is also compared with our proposal using a dataset containing a cloud of outliers and severe multicollinearity. The proposed biased-robust method outperforms the published technique both in simulation and the example.

Original languageEnglish (US)
Pages (from-to)1-22
Number of pages22
JournalJournal of Statistical Computation and Simulation
Issue number1
StatePublished - 1996


  • Bounded influence
  • Breakdown point
  • Efficiency
  • Montecarlo simulation
  • Ridge regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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