A novel, divergence based, regression for compositional data

Michail Tsagris

MPRA Paper from University Library of Munich, Germany

Abstract: In compositional data, an observation is a vector with non-negative components which sum to a constant, typically 1. Data of this type arise in many areas, such as geology, archaeology, biology, economics and political science among others. The goal of this paper is to propose a new, divergence based, regression modelling technique for compositional data. To do so, a recently proved metric which is a special case of the Jensen-Shannon divergence is employed. A strong advantage of this new regression technique is that zeros are naturally handled. An example with real data and simulation studies are presented and are both compared with the log-ratio based regression suggested by Aitchison in 1986.

Keywords: compositional data; Jensen-Shannon divergence; regression; zero values; φ-divergence (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2015-04-17
New Economics Papers: this item is included in nep-cse and nep-ecm
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Published in Proceedings of the 28th Panhellenic Statistics Conference (2015): pp. 430-444

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