ArticleBiometrics2026
Reduced varying coefficient models for regional quantile regression with multiple responses.
Article in Biometrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
Analyzing multiple outcome variables via regional quantile regression in high-dimensional settings poses significant statistical and computational challenges. In this paper, we propose a new framework that models multivariate quantile varying coefficients using principal component functions, enforcing a low-rank structure on the coefficient matrix to achieve parsimony and interpretability. Our approach augments this representation with a KNN-fused LASSO penalty to capture shared dynamic patterns and identify latent clusters within the principal components. Through comprehensive simulation studies, we demonstrate that our method consistently provides accurate estimates and robust performance under various high-dimensional scenarios. We further illustrate its practical utility with two real-world health datasets, where our approach uncovers complex, quantile-specific associations between predictors and multiple correlated outcomes across a time index.
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