Empirical Bayes hierarchical models for regularizing maximum likelihood estimation in the matrix Gaussian Procrustes problem

DOI10.1073/pnas.0508445103zbMath1160.62303OpenAlexW2103956655WikidataQ34001173 ScholiaQ34001173MaRDI QIDQ5385885

Deborah S. Wuttke, Douglas L. Theobald

Publication date: 7 May 2008

Published in: Proceedings of the National Academy of Sciences (Search for Journal in Brave)

Full work available at URL: https://europepmc.org/articles/pmc1664551

zbMATH Keywords

heteroscedasticityleast-squaresProcrustes analysismorphometricssuperpositions

Mathematics Subject Classification ID

Bayesian inference (62F15) Empirical decision procedures; empirical Bayes procedures (62C12)

Related Items (6)

Generation of three-dimensional random rotations in fitting and matching problems ⋮ On matrix-variate regression analysis ⋮ Bayesian alignment of similarity shapes ⋮ Hypothesis Testing for the Covariance Matrix in High-Dimensional Transposable Data with Kronecker Product Dependence Structure ⋮ Learning shape metrics with Monte Carlo optimization ⋮ Procrustes analysis for high-dimensional data

Cites Work

• The mle algorithm for the matrix normal distribution
• Fish Shape Analysis Using Landmarks
• Evolutionary Bioinformatics
• Euclidean distance matrix analysis (EDMA): Estimation of mean form and mean form difference

This page was built for publication: Empirical Bayes hierarchical models for regularizing maximum likelihood estimation in the matrix Gaussian Procrustes problem