ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2017
Regression Models on Riemannian Symmetric Spaces.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
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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
10 citing papers in PubMed.
- Shape-Based Partially Linear Single-Index Cox Model for Alzheimer's Disease Conversion.Statistics in medicine · 2026Article
- Wasserstein F-tests for Fréchet regression on Bures-Wasserstein manifolds.Journal of machine learning research : JMLR · 2025Article
- Nonparametric Statistical Inference via Metric Distribution Function in Metric Spaces.Journal of the American Statistical Association · 2024Article
- Statistical Learning Methods for Neuroimaging Data Analysis with Applications.Annual review of biomedical data science · 2023Review
- Ball Covariance: A Generic Measure of Dependence in Banach Space.Journal of the American Statistical Association · 2020Article
- LOCALIZING DIFFERENTIALLY EVOLVING COVARIANCE STRUCTURES VIA SCAN STATISTICS.Quarterly of applied mathematics · 2019Article
- Nonparametric Bayes Models of Fiber Curves Connecting Brain Regions.Journal of the American Statistical Association · 2019Article
- Riemannian Regression and Classification Models of Brain Networks Applied to Autism.Connectomics in neuroImaging : second international workshop, CNI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018 : proceedings. CNI (Workshop) (2nd : 2018 : Granada, Spain) · 2018Article
- Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2017Article
- Conditional local distance correlation for manifold-valued data.Information processing in medical imaging : proceedings of the ... conference · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The aim of this paper is to develop a general regression framework for the analysis of manifold-valued response in a Riemannian symmetric space (RSS) and its association with multiple covariates of interest, such as age or gender, in Euclidean space. Such RSS-valued data arises frequently in medical imaging, surface modeling, and computer vision, among many others. We develop an intrinsic regression model solely based on an intrinsic conditional moment assumption, avoiding specifying any parametric distribution in RSS. We propose various link functions to map from the Euclidean space of multiple covariates to the RSS of responses. We develop a two-stage procedure to calculate the parameter estimates and determine their asymptotic distributions. We construct the Wald and geodesic test statistics to test hypotheses of unknown parameters. We systematically investigate the geometric invariant property of these estimates and test statistics. Simulation studies and a real data analysis are used to evaluate the finite sample properties of our methods.
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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.