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Ordinary Least Squares for Histogram Data Based on Wasserstein Distance

In: Proceedings of COMPSTAT'2010

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  • Rosanna Verde

    (Seconda Universitá degli Studi di Napoli, Dipartimento di Studi Europei e Mediterranei)

  • Antonio Irpino

    (Seconda Universitá degli Studi di Napoli, Dipartimento di Studi Europei e Mediterranei)

Abstract

Histogram data is a kind of symbolic representation which allows to describe an individual by an empirical frequency distribution. In this paper we introduce a linear regression model for histogram variables. We present a new Ordinary Least Squares approach for a linear model estimation, using the Wasserstein metric between histograms. In this paper we suppose that the regression coefficient are scalar values. After having illustrated the concurrent approaches, we corroborate the proposed estimation method by an application on a real dataset.

Suggested Citation

  • Rosanna Verde & Antonio Irpino, 2010. "Ordinary Least Squares for Histogram Data Based on Wasserstein Distance," Springer Books, in: Yves Lechevallier & Gilbert Saporta (ed.), Proceedings of COMPSTAT'2010, pages 581-588, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2604-3_60
    DOI: 10.1007/978-3-7908-2604-3_60
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