ArticleStatistics in medicine2026
Regularized Tensor Quantile Regression With Applications to Neuroimaging Data Analysis.
Article in Statistics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
This article proposes a regularized linear quantile regression model with a scalar response and tensor-valued covariates. Our model uniquely regularizes the parameters of a low-dimensional tensor effect decomposition through the tensor estimate rather than directly through the decomposition's parameters. We establish the computational and statistical properties of the proposed algorithm and estimators, both of which require separate treatment due to the quantile loss function. Simulation studies demonstrate the superiority of our model over existing tensor frameworks when traditional regression assumptions are violated. A real-world neuroimaging analysis further highlights the interpretability benefits of our approach.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.