The $α$--regression for compositional data: a unified framework for standard, temporal and spatial regression models including compositional predictors
组成数据的 α 回归：涵盖时间、空间及组成型解释变量的统一框架
We revisit the $α$--regression framework for compositional data. We formulate $α$--regression as a non--linear least squares problem, study its asymptotic properties, and provide efficient estimation via the Levenberg--Marquardt algorithm. We then propose a permutation--based hypothesis testing procedure, derive marginal effects for interpretation, and provide a visual inspection of the effect of each predictor. We further discuss robustified versions, the inclusion of…