Using observed confidence levels to perform principal component analyses

Alan M. Polansky, Santu Ghosh

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


ABSTRACT: This paper focuses on applying the method of observed confidence levels to problems commonly encountered in principal component analyses. In particular, we focus on assigning levels of confidence to the number of components that explain a specified proportion of variation in the original data. Approaches based on the normal model as well as a non parametric model are explored. The usefulness of the methods are discussed using an example and an empirical study.

Original languageEnglish (US)
Pages (from-to)3596-3611
Number of pages16
JournalCommunications in Statistics - Theory and Methods
Issue number12
StatePublished - Jun 17 2016


  • Eigenvalue
  • Eigenvector
  • Multivariate analysis
  • Non parametric bootstrap
  • Parametric bootstrap

ASJC Scopus subject areas

  • Statistics and Probability


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