Evaluation of the predictive performance of biased regression estimators

David J. Friedman, Douglas C. Montgomery

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Regression models are widely used in forecasting, either directly as prediction equations, or indirectly as the basis of other procedures. The predictive performance of a regression model can be adversely affected by both multicollinearity and high‐leverage data points. Although biased estimation procedures have been proposed as an alternative to least squares, there has been little analysis of the predictive performance of the resulting equations. This paper discusses the predictive performance of various biased estimators, emphasizing the concept that the predictive region, as well as the strength of the multicollinearity, dictates the choice of appropriate coefficient estimators.

Original languageEnglish (US)
Pages (from-to)153-163
Number of pages11
JournalJournal of Forecasting
Issue number2
StatePublished - 1985
Externally publishedYes


  • Biased estimation
  • Multicollinearity
  • Prediction Regression analysis

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications
  • Strategy and Management
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research


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