Principal Component Analysis for Special Types of Data

  • I. T. Jolliffe
Part of the Springer Series in Statistics book series (SSS)


In much of statistical inference, it is assumed that a data set consists of n independent observations on one or more random variables, x, and this assumption is often implicit when a PCA is done. Another assumption which also may be made implicitly is that x consists of continuous variables, with perhaps the stronger assumption of multivariate normality if we require to make some formal inference for the PCs.


Principal Component Analysis Covariance Matrix Singular Value Decomposition Correspondence Analysis Time Series Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1986

Authors and Affiliations

  • I. T. Jolliffe
    • 1
  1. 1.Mathematical InstituteUniversity of KentKentEngland

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