Regression for compositional data by using distributions defined on the hypersphere

Citations

Cited by:

1. Ulrich B. Morawetz & H. Allen Klaiber, 2025. "Regression analysis with independent variables in shares: a guide and an empirical example," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 52(1), pages 63-98, February.
2. 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.
3. Tsagris, Michail, 2014. "The k-NN algorithm for compositional data: a revised approach with and without zero values present," MPRA Paper 65866, University Library of Munich, Germany.
4. Shogo Kato & Shinto Eguchi, 2016. "Robust estimation of location and concentration parameters for the von Mises–Fisher distribution," Statistical Papers, Springer, vol. 57(1), pages 205-234, March.
5. Xiongtao Dai & Zhenhua Lin & Hans‐Georg Müller, 2021. "Modeling sparse longitudinal data on Riemannian manifolds," Biometrics, The International Biometric Society, vol. 77(4), pages 1328-1341, December.
6. Daisuke Kurisu & Yuta Okamoto & Taisuke Otsu, 2026. "Lee Bounds for Random Objects," Papers 2601.09453, arXiv.org.
7. Michail Tsagris, 2018. "Modelling Structural Zeros in Compositional Data," Working Papers 1803, University of Crete, Department of Economics.
8. Tsagris, Michail, 2015. "Regression analysis with compositional data containing zero values," MPRA Paper 67868, University Library of Munich, Germany.
9. Juan José Egozcue & Vera Pawlowsky-Glahn, 2019. "Compositional data: the sample space and its structure," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 599-638, September.
10. Takahiro Yoshida & Morito Tsutsumi, 2018. "On the effects of spatial relationships in spatial compositional multivariate models," Letters in Spatial and Resource Sciences, Springer, vol. 11(1), pages 57-70, March.
11. Morais, Joanna & Simioni, Michel & Thomas-Agnan, Christine, 2016. "A tour of regression models for explaining shares," TSE Working Papers 16-742, Toulouse School of Economics (TSE).
12. Yoon, Changwon & Choi, Hyunbin & Ahn, Jeongyoun, 2025. "Kernel density estimation for compositional data with zeros via hypersphere mapping," Computational Statistics & Data Analysis, Elsevier, vol. 212(C).
13. J. L. Scealy & A. H. Welsh, 2017. "A Directional Mixed Effects Model for Compositional Expenditure Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 24-36, January.
14. Tsagris, Michail, 2015. "A novel, divergence based, regression for compositional data," MPRA Paper 72769, University Library of Munich, Germany.
15. Jeong Min Jeon & Ingrid Van Keilegom, 2024. "Density estimation and regression analysis on hyperspheres in the presence of measurement error," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(2), pages 513-556, June.
16. Xu, Jiazhen & Scealy, Janice L. & Wood, Andrew T.A. & Zou, Tao, 2025. "Generalized score matching," Journal of Multivariate Analysis, Elsevier, vol. 210(C).
17. Haitao Chai & Hongmei Jiang & Lu Lin & Lei Liu, 2018. "A marginalized two-part Beta regression model for microbiome compositional data," PLOS Computational Biology, Public Library of Science, vol. 14(7), pages 1-16, July.
18. Napoleón Vargas Jurado & Kent M. Eskridge & Stephen D. Kachman & Ronald M. Lewis, 2018. "Using a Bayesian Hierarchical Linear Mixing Model to Estimate Botanical Mixtures," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(2), pages 190-207, June.
19. Yu, Zehao & Huang, Xianzheng, 2025. "Regression analysis of elliptically symmetric directional data," Computational Statistics & Data Analysis, Elsevier, vol. 208(C).
20. Tsagris, Michail & Preston, Simon & T.A. Wood, Andrew, 2016. "Improved classi cation for compositional data using the $\alpha$-transformation," MPRA Paper 67657, University Library of Munich, Germany.
21. Michail Tsagris & Simon Preston & Andrew T. A. Wood, 2016. "Improved Classification for Compositional Data Using the α-transformation," Journal of Classification, Springer;The Classification Society, vol. 33(2), pages 243-261, July.
22. J. L. Scealy & Patrice de Caritat & Eric C. Grunsky & Michail T. Tsagris & A. H. Welsh, 2015. "Robust Principal Component Analysis for Power Transformed Compositional Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 136-148, March.
23. M. Templ & K. Hron & P. Filzmoser, 2017. "Exploratory tools for outlier detection in compositional data with structural zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 734-752, March.
24. Kristoffer H. Hellton, 2023. "Penalized angular regression for personalized predictions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(1), pages 184-212, March.
25. Zhu, Changbo & Müller, Hans-Georg, 2024. "Spherical autoregressive models, with application to distributional and compositional time series," Journal of Econometrics, Elsevier, vol. 239(2).
26. Jiajia Chen & Xiaoqin Zhang & Shengjia Li, 2017. "Multiple linear regression with compositional response and covariates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2270-2285, September.