ArticleJournal of the American Statistical Association2017
Generalized Scalar-on-Image Regression Models via Total Variation.
Article in Journal of the American Statistical Association, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Interpretable Scalar-on-Image Linear Regression Models via the Generalized Dantzig Selector.Journal of the American Statistical Association · 2026Article
- High-dimensional partial linear model with trend filtering.Electronic journal of statistics · 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
- Bayesian thresholded modeling for integrating brain node and network predictors.Biostatistics (Oxford, England) · 2025Article
- Identification of Regions of Interest in Neuroimaging Data With Irregular Boundary Based on Semiparametric Transformation Models and Interval-Censored Outcomes.Statistics in medicine · 2025Article
- Spatial Adaptive Selection using Binary Conditional Autoregressive Model with Application to Brain-Computer Interface.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer's disease.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2024Article
- Robust High-Dimensional Regression with Coefficient Thresholding and its Application to Imaging Data Analysis.Journal of the American Statistical Association · 2024Article
- Multi-task Learning with High-Dimensional Noisy Images.Journal of the American Statistical Association · 2024Article
- Comparison of two statistical methodologies for a binary classification problem of two-dimensional images.Journal of applied statistics · 2024Article
- Identifying regions of interest in mammogram images.Statistical methods in medical research · 2023Article
- Minimax Powerful Functional Analysis of Covariance Tests: with Application to Longitudinal Genome-Wide Association Studies.Scandinavian journal of statistics, theory and applications · 2023Article
- Partially Observed Dynamic Tensor Response Regression.Journal of the American Statistical Association · 2023Article
- SCALAR ON NETWORK REGRESSION VIA BOOSTING.The annals of applied statistics · 2022Article
- Article
- From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas.Journal of multivariate analysis · 2022Article
- High-Dimensional Spatial Quantile Function-on-Scalar Regression.Journal of the American Statistical Association · 2022Article
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- TENSOR QUANTILE REGRESSION WITH APPLICATION TO ASSOCIATION BETWEEN NEUROIMAGES AND HUMAN INTELLIGENCE.The annals of applied statistics · 2021Article
- Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation.Journal of the American Statistical Association · 2021Article
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2 authors.
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Abstract
The use of imaging markers to predict clinical outcomes can have a great impact in public health. The aim of this paper is to develop a class of generalized scalar-on-image regression models via total variation (GSIRM-TV), in the sense of generalized linear models, for scalar response and imaging predictor with the presence of scalar covariates. A key novelty of GSIRM-TV is that it is assumed that the slope function (or image) of GSIRM-TV belongs to the space of bounded total variation in order to explicitly account for the piecewise smooth nature of most imaging data. We develop an efficient penalized total variation optimization to estimate the unknown slope function and other parameters. We also establish nonasymptotic error bounds on the excess risk. These bounds are explicitly specified in terms of sample size, image size, and image smoothness. Our simulations demonstrate a superior performance of GSIRM-TV against many existing approaches. We apply GSIRM-TV to the analysis of hippocampus data obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset.
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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.