ArticleJournal of the American Statistical Association2022
High-Dimensional Spatial Quantile Function-on-Scalar Regression.
Article in Journal of the American Statistical Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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.
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Who cites it
3 citing papers in PubMed.
- Scalable Bayesian Image-on-Scalar Regression for Population-Scale Neuroimaging Data Analysis.Journal of the American Statistical Association · 2026Article
- Federated Function-on-function Regression with an Efficient Gradient Boosting Algorithm for Privacy-Preserving Telemedicine.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2026Article
- Statistical Learning Methods for Neuroimaging Data Analysis with Applications.Annual review of biomedical data science · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This article develops a novel spatial quantile function-on-scalar regression model, which studies the conditional spatial distribution of a high-dimensional functional response given scalar predictors. With the strength of both quantile regression and copula modeling, we are able to explicitly characterize the conditional distribution of the functional or image response on the whole spatial domain. Our method provides a comprehensive understanding of the effect of scalar covariates on functional responses across different quantile levels and also gives a practical way to generate new images for given covariate values. Theoretically, we establish the minimax rates of convergence for estimating coefficient functions under both fixed and random designs. We further develop an efficient primal-dual algorithm to handle high-dimensional image data. Simulations and real data analysis are conducted to examine the finite-sample performance.
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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 OpenQuestion graph.