Partial least squares prediction in high-dimensional regression

R. Dennis Cook, Liliana Forzani

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


We study the asymptotic behavior of predictions from partial least squares (PLS) regression as the sample size and number of predictors diverge in various alignments. We show that there is a range of regression scenarios where PLS predictions have the usual root-n convergence rate, even when the sample size is substantially smaller than the number of predictors, and an even wider range where the rate is slower but may still produce practically useful results. We show also that PLS predictions achieve their best asymptotic behavior in abundant regressions where many predictors contribute information about the response. Their asymptotic behavior tends to be undesirable in sparse regressions where few predictors contribute information about the response.

Original languageEnglish (US)
Pages (from-to)884-908
Number of pages25
JournalAnnals of Statistics
Issue number2
StatePublished - Apr 2019

Bibliographical note

Publisher Copyright:
© Institute of Mathematical Statistics, 2019


  • Abundant regressions
  • Dimension reduction
  • Sparse regressions


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