Model-free variable selection

Lexin Li, R. Dennis Cook, Christopher J. Nachtsheim

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


The importance of variable selection in regression has grown in recent years as computing power has encouraged the modelling of data sets of ever-increasing size. Data mining applications in finance, marketing and bioinformatics are obvious examples. A limitation of nearly all existing variable selection methods is the need to specify the correct model before selection. When the number of predictors is large, model formulation and validation can be difficult or even infeasible. On the basis of the theory of sufficient dimension reduction, we propose a new class of model-free variable selection approaches. The methods proposed assume no model of any form, require no nonparametric smoothing and allow for general predictor effects. The efficacy of the methods proposed is demonstrated via simulation, and an empirical example is given.

Original languageEnglish (US)
Pages (from-to)285-299
Number of pages15
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Issue number2
StatePublished - Apr 18 2005


  • Model selection
  • Sliced inverse regression
  • Stepwise regression
  • Sufficient dimension reduction

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