AUTHOR

Alzeley Omar

https://9b9ec758578b3ee0d46b-305404f9eb35eaf4130aa2d106c6a91c.ssl.cf3.rackcdn.com/597/main_gallery/images//p1gonsk4v2jbs15vq18017om1f47a.jpg
Statistics , Data Analytics - 2026

Scalable approximation of the transformation-free linear simplicial-simplicial regression via constrained iterative reweighted least squares

Simplicia-simplicial regression concerns statistical modeling scenarios in which both the predictors and the responses are vectors constrained to lie on the simplex. Fiksel et al. (2022) introduced a transformationfree linear regression framework for this setting, wherein the regression coefficients are estimated by minimizing the Kullback-Leibler divergence between the observed and fitted compositions, using an expectation-maximization (EM) algorithm for optimization. In this work, we reformulate the problem as a constrained logistic regression model, in line with the methodological perspective of Tsagris (2025), and we obtain parameter estimates via constrained iteratively reweighted least squares. Simulation results indicate that the proposed procedure substantially improves computational efficiency-yielding speed gains ranging from 6×−−326×-while providing estimates that closely approximate those obtained from the EM-based approach.

https://9b9ec758578b3ee0d46b-305404f9eb35eaf4130aa2d106c6a91c.ssl.cf3.rackcdn.com/597/main_gallery/images//p1gonsk4v22r01mb1irl12ar1sh35.jpg
Statistics , Applied Research - 2023

Circular and Spherical Projected Cauchy Distributions

Two new distributions are proposed: the circular projected and the spherical projected Cauchy distributions. A special case of the circular projected Cauchy coincides with the wrapped Cauchy distribution, and for this, a generalization is suggested that offers better fit via the inclusion of an extra parameter. For the spherical case, by imposing two conditions on the scatter matrix we end up with an elliptically symmetric distribution.

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