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Penalized composite quantile regression model for compositional data
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DOI：10.1080/02331888.2026.2632740.png)
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Abstract
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Motivated by challenges in gut microbiome data analysis, we propose a penalized composite quantile regression (CQR) method with linear constraints that ensures non-crossing quantiles across multiple levels. The method is particularly well suited for compositional data, while the framework naturally extends to more general constrained penalized CQR settings. By integrating information across multiple quantiles, the proposed approach improves estimation efficiency, especially in the tails, while preserving robustness without requiring the errors to follow any particular distribution. To solve the associated optimization problem, we develop an efficient ADMM algorithm. Simulation studies and a microbiome application investigating the relationship between gut composition and human body mass index illustrate the robustness, accuracy, and practical utility of the method.
Keywords:
Composite quantile regression
penalized and constrained
non-crossing quantiles
compositional data
ADMM algorithm
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APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
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