Prediction using regression models with multicollinear predictor variables

Douglas Montgomery, David J. Friedman

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

This article explores the use of regression models in this context when the regressor or predictor variables exhibit multicollinearity, or near-linear dependence. Several biased estimation methods are described and evaluated, including a new method for selecting the biasing parameter in ordinary ridge regression. A simulation study is performed to provide some guidelines for the choice of an estimation method.

Original languageEnglish (US)
Pages (from-to)73-85
Number of pages13
JournalIIE Transactions (Institute of Industrial Engineers)
Volume25
Issue number3
StatePublished - May 1993

Fingerprint

Regression model
Predictors
Prediction
Simulation study
Ridge regression
Multicollinearity

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

Prediction using regression models with multicollinear predictor variables. / Montgomery, Douglas; Friedman, David J.

In: IIE Transactions (Institute of Industrial Engineers), Vol. 25, No. 3, 05.1993, p. 73-85.

Research output: Contribution to journalArticle

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