Logratio Approach to Distributional Modeling

Peter Filzmoser (), Karel Hron () and Alessandra Menafoglio ()
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Peter Filzmoser: Vienna University of Technology, Institute of Statistics and Mathematical Methods in Economics
Karel Hron: Palacký University, Department of Mathematical Analysis and Applications of Mathematics
Alessandra Menafoglio: Politecnico di Milano, MOX, Department of Mathematics

A chapter in Advances in Contemporary Statistics and Econometrics, 2021, pp 451-470 from Springer

Abstract: Abstract Distributional data, such as age distributions of populations, can be treated as continuous or discrete data, but the main interest is in the relative information, e.g., in terms of ratios (or logratios) between the different age classes. Here we present a unifying framework for the discrete and the continuous case based on the theory of Bayes spaces. While the discrete case is more widely treated in the literature, the continuous case allows to make a link to functional data analysis. Moreover, the methodological framework of Bayes spaces can also be used to develop methods for analyzing several distributional variables simultaneously. It turns out that the centered logratio transformation is a convenient tool for practical computations. Two real data examples illustrate the usefulness of this framework.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-73249-3_23

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DOI: 10.1007/978-3-030-73249-3_23

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