A principal component analysis for trees

DOI10.1214/09-AOAS263zbMath1184.62100arXiv0810.0944OpenAlexW2963432779MaRDI QIDQ965130

Gábor Pataki, Elizabeth Bullitt, Burcu Aydın, Haonan Wang, James Stephen Marron

Publication date: 21 April 2010

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0810.0944

zbMATH Keywords

principal component analysispopulation structureobject oriented data analysistree-linestree-structured objects

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of graph theory (05C90)

Related Items

Statistical shape analysis of simplified neuronal trees, Dimension reduction in principal component analysis for trees, Statistical shape analysis of brain arterial networks (BAN), Centroid based tree-structured data clustering using vertex/edge overlap and graph edit distance, Overview of object oriented data analysis, OODA of graph and tree-structured data, Averages of unlabeled networks: geometric characterization and asymptotic behavior, Pareto quantiles of unlabeled tree objects, Towards quotient barycentric subspaces, Visualizing the structure of large trees, Principal components analysis in the space of phylogenetic trees, A Nonparametric Regression Model With Tree-Structured Response, Diffusion on some simple stratified spaces, Tree-oriented analysis of brain artery structure, Statistical analysis and modeling of the geometry and topology of plant roots, Model-free two-sample test for network-valued data

Uses Software

• fda (R)

Cites Work

• Metric models for random graphs
• Heuristics of instability and stabilization in model selection
• Applied functional data analysis. Methods and case studies
• Object oriented data analysis: sets of trees
• Functional data analysis.
• Nonparametric functional data analysis. Theory and practice.
• Algebraic Statistics for Computational Biology
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