Scaled Predictor Envelopes and Partial Least-Squares Regression

R. Dennis Cook, Zhihua Su

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Partial least squares (PLS) is a widely used method for prediction in applied statistics, especially in chemometrics applications. However, PLS is not invariant or equivariant under scale transformations of the predictors, which tends to limit its scope to regressions in which the predictors are measured in the same or similar units. Cook, Helland, and Su (2013) built a connection between nascent envelope methodology and PLS, allowing PLS to be addressed in a traditional likelihood-based framework. In this article, we use the connection between PLS and envelopes to develop a new method-scaled predictor envelopes (SPE)-that incorporates predictor scaling into PLS-type applications. By estimating the appropriate scales, the SPE estimators can offer efficiency gains beyond those given by PLS, and further reduce prediction errors. Simulations and an example are given to support the theoretical claims.

Original languageEnglish (US)
Pages (from-to)155-165
Number of pages11
Issue number2
StatePublished - Apr 2 2016

Bibliographical note

Publisher Copyright:
© 2016 American Statistical Association and the American Society for Quality.


  • Dimension reduction
  • Envelope model
  • Grassmann manifold
  • Scale invariance


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