ARC framework models high-dimensional compositional data with exact zeros by treating compositions as directions of latent vectors with an explicit active-set process on the hypersphere.
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A new geographically weighted penalized compositional regression model with pairwise fusion penalty is proposed to handle spatial heterogeneity and compositional covariates, demonstrated on U.S. income and COPD data.
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Composition as Direction: An Active-Set Ray-Based Model for Sparse High-Dimensional Compositional Data
ARC framework models high-dimensional compositional data with exact zeros by treating compositions as directions of latent vectors with an explicit active-set process on the hypersphere.
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Linking COPD Prevalence with Income Distribution: A Spatial Heterogeneous Compositional Regression via Geographically Weighted Penalized Approach
A new geographically weighted penalized compositional regression model with pairwise fusion penalty is proposed to handle spatial heterogeneity and compositional covariates, demonstrated on U.S. income and COPD data.