Envelopes and partial least squares regression

R. D. Cook, I. S. Helland, Z. Su

Research output: Contribution to journalArticlepeer-review

86 Scopus citations


Summary: We build connections between envelopes, a recently proposed context for efficient estimation in multivariate statistics, and multivariate partial least squares (PLS) regression. In particular, we establish an envelope as the nucleus of both univariate and multivariate PLS, which opens the door to pursuing the same goals as PLS but using different envelope estimators. It is argued that a likelihood-based envelope estimator is less sensitive to the number of PLS components that are selected and that it outperforms PLS in prediction and estimation.

Original languageEnglish (US)
Pages (from-to)851-877
Number of pages27
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Issue number5
StatePublished - Nov 2013


  • Dimension reduction
  • Envelope models
  • Envelopes
  • Maximum likelihood estimation
  • Partial least squares
  • SIMPLS algorithm


Dive into the research topics of 'Envelopes and partial least squares regression'. Together they form a unique fingerprint.

Cite this