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A novel, divergence based, regression for compositional data

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  • Tsagris, Michail

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.

Suggested Citation

  • Tsagris, Michail, 2015. "A novel, divergence based, regression for compositional data," MPRA Paper 72769, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:72769
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    References listed on IDEAS

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    1. Kateri, Maria & Agresti, Alan, 2010. "A generalized regression model for a binary response," Statistics & Probability Letters, Elsevier, vol. 80(2), pages 89-95, January.
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    3. J. L. Scealy & A. H. Welsh, 2011. "Regression for compositional data by using distributions defined on the hypersphere," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 351-375, June.
    4. Gueorguieva, Ralitza & Rosenheck, Robert & Zelterman, Daniel, 2008. "Dirichlet component regression and its applications to psychiatric data," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5344-5355, August.
    5. José M. R. Murteira & Joaquim J. S. Ramalho, 2016. "Regression Analysis of Multivariate Fractional Data," Econometric Reviews, Taylor & Francis Journals, vol. 35(4), pages 515-552, April.
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    Cited by:

    1. Antoine Burg & Christophe Dutang, 2026. "Closed-form estimators for multivariate regressions models -a single categorical variable approach," Post-Print hal-05539060, HAL.
    2. Antoine Burg & Christophe Dutang, 2026. "Closed-form estimators for multivariate regressions models: a single categorical variable approach," Computational Statistics, Springer, vol. 41(3), pages 1-23, April.
    3. Michail Tsagris & Yannis Pantazis, 2026. "The α–regression for compositional data: a unified framework for standard, spatially-lagged, spatial autoregressive and geographically-weighted regression models," Working Papers 2603, University of Crete, Department of Economics.

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    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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