Depthgram: visualizing outliers in high-dimensional functional data with application to fMRI data exploration

DOI10.1002/sim.9342zbMath1547.62119MaRDI QIDQ6628342

Yasser Alemán-Gómez, Manuel Desco, Ana Arribas-Gil, Juan J. Romo, Unnamed Author

Publication date: 29 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

dimensionality reductionfMRIdata visualizationmultidimensional outliersfunctional depth

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Multivariate Functional Data Visualization and Outlier Detection
• Robust archetypoids for anomaly detection in big functional data
• Multivariate functional outlier detection
• M. Hubert, P. Rousseeuw and P. Segaert: ``Multivariate functional outlier detection
• A half-region depth for functional data
• The random Tukey depth
• Component-wise outlier detection methods for robustifying multivariate functional samples
• A Measure of Directional Outlyingness With Applications to Image Data and Video
• Depth Measures for Multivariate Functional Data
• Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains
• Multivariate Functional Halfspace Depth
• On the Concept of Depth for Functional Data
• Linear Manifold Modelling of Multivariate Functional Data
• Discussion of ``Multivariate functional outlier detection
• Exact fast computation of band depth for large functional datasets: how quickly can one million curves be ranked?

Related Items (2)

Novel whitening approaches in functional settings ⋮ Robust Multivariate Functional Control Chart

This page was built for publication: Depthgram: visualizing outliers in high-dimensional functional data with application to fMRI data exploration