Robust regression with high coverage

David J. Olive, Douglas M. Hawkins

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


An important parameter for several high breakdown regression algorithm estimators is the number of cases given weight one, called the coverage of the estimator. Increasing the coverage is believed to result in a more stable estimator, but the price paid for this stability is greatly decreased resistance to outliers. A simple modification of the algorithm can greatly increase the coverage and hence its statistical performance while maintaining high outlier resistance.

Original languageEnglish (US)
Pages (from-to)259-266
Number of pages8
JournalStatistics and Probability Letters
Issue number3
StatePublished - Jul 1 2003

Bibliographical note

Funding Information:
This research was supported by NSF grants DMS 9806584 and DMS 0202922. The authors are grateful to the editor and a referee for a number of helpful suggestions for improvement in the article.


  • Elemental sets
  • LMS
  • LTA
  • LTS
  • Outliers


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