Principal Components Analysis

  • Wolfgang Karl HärdleEmail author
  • Léopold Simar


Chapter 10 presented the basic geometric tools needed to produce a lower dimensional description of the rows and columns of a multivariate data matrix. Principal components analysis has the same objective with the exception that the rows of the data matrix \({{\mathcal {X}}}\) will now be considered as observations from a p-variate random variable X. The principle idea of reducing the dimension of X is achieved through linear combinations.


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Authors and Affiliations

  1. 1.Ladislaus von Bortkiewicz Chair of StatisticsHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Institute of Statistics, Biostatistics and Actuarial SciencesUniversité Catholique de LouvainLouvain-la-NeuveBelgium

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