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Principal Component Analysis

In: Multivariate Statistics

Author

Listed:
  • Wolfgang Karl Härdle

    (Humboldt-Universität zu Berlin, C.A.S.E. Centre f. Appl. Stat. & Econ. School of Business and Economics)

  • Zdeněk Hlávka

    (Charles University in Prague, Faculty of Mathematics and Physics Department of Statistics)

Abstract

This chapter addresses the issue of reducing the dimensionality of a multivariate random variable by using linear combinations (the principal components). The identified principal components are ordered in decreasing order of importance. When applied in practice to a data matrix, the principal components will turn out to be the factors of a transformed data matrix (the data will be centered and eventually standardized).

Suggested Citation

  • Wolfgang Karl Härdle & Zdeněk Hlávka, 2015. "Principal Component Analysis," Springer Books, in: Multivariate Statistics, edition 2, chapter 0, pages 183-203, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-36005-3_11
    DOI: 10.1007/978-3-642-36005-3_11
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