A novel, divergence based, regression for compositional data
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 amongst 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.
Cite
@article{arxiv.1511.07600,
title = {A novel, divergence based, regression for compositional data},
author = {Michail Tsagris},
journal= {arXiv preprint arXiv:1511.07600},
year = {2015}
}
Comments
This is a preprint of the paper accepted for publication in the Proceedings of the 28th Panhellenic Statistics Conference, 15-18/4/2015, Athens, Greece