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.
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Verde, R., Irpino, A. (2010). Ordinary Least Squares for Histogram Data Based on Wasserstein Distance. In: Lechevallier, Y., Saporta, G. (eds) Proceedings of COMPSTAT'2010. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2604-3_60
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DOI: https://doi.org/10.1007/978-3-7908-2604-3_60
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Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-2603-6
Online ISBN: 978-3-7908-2604-3
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