Influence function analysis for partial least squares with uncorrelated components

Kjell Johnson, William Rayens

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Influence theory has been studied extensively in multivariate analysis and detailed results are available for a host of multivariate techniques, including principal components, canonical correlations, and linear discrimination. In this article, the first such results are derived for partial least squares (PLS). In particular, classical perturbation theory is employed to produce theoretical and empirical influence functions for PLS under the constraint of uncorrelated scores. These influence functions are carefully interpreted and then applied to a protein analysis problem.

Original languageEnglish
Pages (from-to)65-93
Number of pages29
Issue number1
StatePublished - Feb 2006


  • Empirical influence function
  • Influence function
  • Partial least squares

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Influence function analysis for partial least squares with uncorrelated components'. Together they form a unique fingerprint.

Cite this