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Moving to Higher Dimensions

In: Applied Multivariate Statistical Analysis

Author

Listed:
  • Wolfgang Härdle

    (Humboldt-Universität zu Berlin, CASE — Center for Applied Statistics and Economics, Institut für Statistik und Ökonometrie)

  • Léopold Simar

    (Université Catholique Louvain, Inst. Statistique)

Abstract

We have seen in the previous chapters how very simple graphical devices can help in understanding the structure and dependency of data. The graphical tools were based on either univariate (bivariate) data representations or on “slick” transformations of multivariate information perceivable by the human eye. Most of the tools are extremely useful in a modelling step, but unfortunately, do not give the full picture of the data set. One reason for this is that the graphical tools presented capture only certain dimensions of the data and do not necessarily concentrate on those dimensions or subparts of the data under analysis that carry the maximum structural information. In Part III of this book, powerful tools for reducing the dimension of a data set will be presented. In this chapter, as a starting point, simple and basic tools are used to describe dependency. They are constructed from elementary facts of probability theory and introductory statistics (for example, the covariance and correlation between two variables).

Suggested Citation

  • Wolfgang Härdle & Léopold Simar, 2003. "Moving to Higher Dimensions," Springer Books, in: Applied Multivariate Statistical Analysis, chapter 3, pages 81-118, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-05802-2_3
    DOI: 10.1007/978-3-662-05802-2_3
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