The development and evaluation of alternative generalized m-estimation techniques

James R. Simpson, Douglas Montgomery

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Least squares estimation is a widely used regression technique. The presence of outliers in data can have an adverse effect on least squares estimates, resulting in a model that does not adequately fit the bulk of the data. Robust regression techniques have been proposed as an alternative to least squares when outliers are present. We develop and evaluate new robust regression procedures and compare their performance to the best alternatives currently available in terms of efficiency, breakdown, and bounded influence. We offer the better performing alternatives as possible methods for use in a robust regression scenario.

Original languageEnglish (US)
Pages (from-to)999-1018
Number of pages20
JournalCommunications in Statistics Part B: Simulation and Computation
Volume27
Issue number4
DOIs
StatePublished - Jan 1 1998

Keywords

  • Bounded influence
  • Breakdown
  • Efficiency
  • Estimates of leverage
  • Iteratively reweighted least squares
  • Monte Carlo simulation
  • Outliers
  • Robust regression estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation

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