ArticleCommunications in statistics: Simulation and computation2017
Nonparametric Bootstrap of Sample Means of Positive-Definite Matrices with an Application to Diffusion-Tensor-Imaging Data Analysis.
Article in Communications in statistics: Simulation and computation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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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Who cites it
3 citing papers in PubMed.
- Article
- Advances in computational and statistical diffusion MRI.NMR in biomedicine · 2019Review
- Lognormal Distributions and Geometric Averages of Symmetric Positive Definite Matrices.International statistical review = Revue internationale de statistique · 2016Article
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Authors and funding
5 authors.
Funding
Abstract
This paper presents nonparametric two-sample bootstrap tests formeans of randomsymmetric positivedefinite (SPD) matrices according to two differentmetrics: the Frobenius (or Euclidean)metric, inherited from the embedding of the set of SPD metrics in the Euclidean set of symmetric matrices, and the canonical metric, which is defined without an embedding and suggests an intrinsic analysis. A fast algorithm is used to compute the bootstrap intrinsic means in the case of the latter. The methods are illustrated in a simulation study and applied to a two-group comparison of means of diffusion tensors (DTs) obtained from a single voxel of registered DT images of children in a dyslexia study.
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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.