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Principal component analysis of interval data: a symbolic data analysis approach

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Summary

The present paper deals with the study of continuous interval data by means of suitable Principal Component Analyses (PCA). Statistical units described by interval data can be assumed as special cases of Symbolic Objects (SO) (Diday, 1987). In Symbolic Data Analysis (SDA), these data are represented as hypercubes. In the present paper, we propose some extensions of the PCA with the aim of representing, in a space of reduced dimensions, images of such hypercubes, pointing out differences and similarities according to their structural features.

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Acknowledgements

This paper has been developed in the ISO-3D Esprit project at DMS of University of Naples “Federico II.”.@@The output in [§7] has been realised with the collaboration of Dr. G. Meccariello (1999).

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Authors and Affiliations

  1. Dipartimento di Matematica e Statistica, Università “Federico II”, Napoli, Italy

    Carlo N. Lauro

  2. Dipartimento di Istituzioni Economiche e Finanziarie, Università di Macerata, Macerata, Italy

    Francesco Palumbo

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  1. Carlo N. Lauro
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  2. Francesco Palumbo
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Lauro, C.N., Palumbo, F. Principal component analysis of interval data: a symbolic data analysis approach. Computational Statistics 15, 73–87 (2000). https://doi.org/10.1007/s001800050038

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