Dynamic GSCANO (generalized structured canonical correlation analysis) with applications to the analysis of effective connectivity in functional neuroimaging data

DOI10.1016/j.csda.2016.03.001zbMath1466.62231OpenAlexW2294073983WikidataQ114671400 ScholiaQ114671400MaRDI QIDQ1659062

Yoshio Takane, Lixing Zhou, Heungsun Hwang

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2016.03.001

zbMATH Keywords

alternating least squares (ALS) algorithmlongitudinal and time series datastructural equation modeling (SEM)brain connectivitydynamic generalized structured component analysis (dynamic GSCA)fMRI (functional magnetic resonance imaging) data

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Related Items (1)

Correlation structure regularization via entropy loss function for high-dimension and low-sample-size data

Uses Software

• GeSCA
• bootstrap

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Dynamic GSCA (generalized structured component analysis) with applications to the analysis of effective connectivity in functional neuroimaging data
• The validity of instruments revisited
• Generalized structured component analysis with latent interactions
• Additive structure in qualitative data: An alternating least squares method with optimal scaling features
• A general solution to Mosier's oblique Procrustes problem
• Generalized structured component analysis
• Fuzzy clusterwise generalized structured component analysis
• A unified approach to multiple‐set canonical correlation analysis and principal components analysis
• RELATIONS BETWEEN TWO SETS OF VARIATES
• Constrained principal component analysis: A comprehensive theory

This page was built for publication: Dynamic GSCANO (generalized structured canonical correlation analysis) with applications to the analysis of effective connectivity in functional neuroimaging data