ArticleJournal of the American Statistical Association2020
Quantile Function on Scalar Regression Analysis for Distributional Data.
Article in Journal of the American Statistical Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- PM2.5/PM10 Forecasting System with Benchmarking of 44 Machine Learning Algorithms and Ensemble Learning Approaches.Sensors (Basel, Switzerland) · 2026Article
- Functional quantile principal component analysis.Biostatistics (Oxford, England) · 2025Article
- FAST VARIABLE SELECTION FOR DISTRIBUTIONAL REGRESSION WITH APPLICATION TO CONTINUOUS GLUCOSE MONITORING DATA.The annals of applied statistics · 2025Article
- On function-on-function linear quantile regression.Journal of applied statistics · 2025Article
- A scalar-on-quantile-function approach for estimating short-term health effects of environmental exposures.Biometrics · 2024Article
- Tumor radiogenomics in gliomas with Bayesian layered variable selection.Medical image analysis · 2023Article
- Statistical Learning Methods for Neuroimaging Data Analysis with Applications.Annual review of biomedical data science · 2023Review
- Distributional data analysis via quantile functions and its application to modeling digital biomarkers of gait in Alzheimer's Disease.Biostatistics (Oxford, England) · 2023Article
- Ultra-Fast Approximate Inference Using Variational Functional Mixed Models.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- TWO-SAMPLE TESTS FOR MULTIVARIATE REPEATED MEASUREMENTS OF HISTOGRAM OBJECTS WITH APPLICATIONS TO WEARABLE DEVICE DATA.The annals of applied statistics · 2022Article
- Scalar on time-by-distribution regression and its application for modelling associations between daily-living physical activity and cognitive functions in Alzheimer's Disease.Scientific reports · 2022Article
- Spike-and-slab type variable selection in the Cox proportional hazards model for high-dimensional features.Journal of applied statistics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Radiomics involves the study of tumor images to identify quantitative markers explaining cancer heterogeneity. The predominant approach is to extract hundreds to thousands of image features, including histogram features comprised of summaries of the marginal distribution of pixel intensities, which leads to multiple testing problems and can miss out on insights not contained in the selected features. In this paper, we present methods to model the entire marginal distribution of pixel intensities via the quantile function as functional data, regressed on a set of demographic, clinical, and genetic predictors to investigate their effects of imaging-based cancer heterogeneity. We call this approach
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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.