2025/11/17 by Michail Tsagris, Tsagris, Michail, Alzeley, Omar
Computer Science · Mathematics · #Advanced Statistical Methods and Models #Computation (stat.CO) #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Inference #Stochastic Gradient Optimization Techniques
paper · pdf · doi:10.48550/arxiv.2511.13296
openalex publication_date 2025/11/17 · openalex created_date 2025/11/19 · openalex updated_date 2026/07/28
Simplicia-simplicial regression concerns statistical modeling scenarios in which both the predictors and the responses are vectors constrained to lie on the simplex. \citefiksel2022 introduced a transformation-free 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 \citetsagris2025, 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.