Variable selection for 1D regression models

David J. Olive, Douglas M. Hawkins

Research output: Contribution to specialist publicationArticle

14 Scopus citations


variable selection, the search for j relevant predictor variables from a group of p candidates, is a standard problem in regression analysis. The class of ID regression models is a broad class that includes generalized linear models. We show that existing variable selection algorithms, originally meant for multiple linear regression and based on ordinary least squares and Mallows's Cp, can also be used for ID models. Graphical aids for variable selection are also provided.

Original languageEnglish (US)
Number of pages8
Specialist publicationTechnometrics
StatePublished - Feb 2005

Bibliographical note

Funding Information:
The authors thank to the editor, associate editor, and referees for a number of helpful suggestions for improvement in the article. Stan Young and Dennis Cook read an earlier version of the manuscript. This work was supported by National Science Foundation grants DMS-02-02922, DMS-03-06304, DMS-98-03622, and ACI 9619020.


  • C
  • Cook's distance
  • Generalized linear model
  • Outlier
  • Regression graphics
  • Single index model


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