Procrustes analysis for high-dimensional data

DOI10.1007/s11336-022-09859-5zbMath1499.62427arXiv2008.04631OpenAlexW3183477416MaRDI QIDQ2103574

Angela Andreella, Livio Finos

Publication date: 9 December 2022

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2008.04631

zbMATH Keywords

von Mises-Fisher distributionhigh-dimensional dataProcrustes analysisfunctional magnetic resonance imagingfunctional alignment

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Estimation in multivariate analysis (62H12) Bayesian inference (62F15) Applications of statistics to psychology (62P15)

Related Items (1)

\textit{Procrustes}-based distances for exploring between-matrices similarity

Uses Software

• R
• JDQR
• JDQZ
• Python
• FSL

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Generalized Procrustes analysis
• Maximum likelihood estimators for the matrix von Mises-Fisher and Bingham distributions
• On semi-orthogonality and a special class of matrices
• Statistics on special manifolds
• Bayesian alignment of similarity shapes
• The orthogonal approximation of an oblique structure in factor analysis
• Information and Exponential Families
• The mle algorithm for the matrix normal distribution
• Templates for the Solution of Algebraic Eigenvalue Problems
• Bayesian Attitude Estimation With the Matrix Fisher Distribution on SO(3)
• Empirical Bayes hierarchical models for regularizing maximum likelihood estimation in the matrix Gaussian Procrustes problem
• On the convergence of some Procrustean averaging algorithms
• Bayesian alignment using hierarchical models, with applications in protein bioinformatics
• Orientation statistics
• Euclidean distance matrix analysis (EDMA): Estimation of mean form and mean form difference

This page was built for publication: Procrustes analysis for high-dimensional data