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Article: Functional data analysis of tree data objects

TitleFunctional data analysis of tree data objects
Authors
KeywordsTree pruning
Support tree
Dyck path
DiProPerm
Branch length
Issue Date2014
Citation
Journal of Computational and Graphical Statistics, 2014, v. 23, n. 2, p. 418-438 How to Cite?
AbstractData analysis on non-Euclidean spaces, such as tree spaces, can be challenging. The main contribution of this article is establishment of a connection between tree-data spaces and the well-developed area of functional data analysis (FDA), where the data objects are curves. This connection comes through two tree representation approaches, the Dyck path representation and the branch length representation. These representations of trees in the Euclidean spaces enable us to exploit the power of FDA to explore statistical properties of tree data objects. Amajor challenge in the analysis is the sparsity of tree branches in a sample of trees. We overcome this issue by using a tree-pruning technique that focuses the analysis on important underlying population structures. This method parallels scale-space analysis in the sense that it reveals statistical properties of tree-structured data over a range of scales. The effectiveness of these new approaches is demonstrated by some novel results obtained in the analysis of brain-artery trees. The scale-space analysis reveals a deeper relationship between structure and age. These methods are the first to find a statistically significant gender difference. Supplementary materials for this article are available online. © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
Persistent Identifierhttp://hdl.handle.net/10722/219748
ISSN
2015 Impact Factor: 1.755
2015 SCImago Journal Rankings: 2.321

 

DC FieldValueLanguage
dc.contributor.authorShen, Dan-
dc.contributor.authorShen, Haipeng-
dc.contributor.authorBhamidi, Shankar-
dc.contributor.authorMuñoz Maldonado, Yolanda-
dc.contributor.authorKim, Yongdai-
dc.contributor.authorMarron, J. S.-
dc.date.accessioned2015-09-23T02:57:52Z-
dc.date.available2015-09-23T02:57:52Z-
dc.date.issued2014-
dc.identifier.citationJournal of Computational and Graphical Statistics, 2014, v. 23, n. 2, p. 418-438-
dc.identifier.issn1061-8600-
dc.identifier.urihttp://hdl.handle.net/10722/219748-
dc.description.abstractData analysis on non-Euclidean spaces, such as tree spaces, can be challenging. The main contribution of this article is establishment of a connection between tree-data spaces and the well-developed area of functional data analysis (FDA), where the data objects are curves. This connection comes through two tree representation approaches, the Dyck path representation and the branch length representation. These representations of trees in the Euclidean spaces enable us to exploit the power of FDA to explore statistical properties of tree data objects. Amajor challenge in the analysis is the sparsity of tree branches in a sample of trees. We overcome this issue by using a tree-pruning technique that focuses the analysis on important underlying population structures. This method parallels scale-space analysis in the sense that it reveals statistical properties of tree-structured data over a range of scales. The effectiveness of these new approaches is demonstrated by some novel results obtained in the analysis of brain-artery trees. The scale-space analysis reveals a deeper relationship between structure and age. These methods are the first to find a statistically significant gender difference. Supplementary materials for this article are available online. © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.-
dc.languageeng-
dc.relation.ispartofJournal of Computational and Graphical Statistics-
dc.subjectTree pruning-
dc.subjectSupport tree-
dc.subjectDyck path-
dc.subjectDiProPerm-
dc.subjectBranch length-
dc.titleFunctional data analysis of tree data objects-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1080/10618600.2013.786943-
dc.identifier.scopuseid_2-s2.0-84901793170-
dc.identifier.volume23-
dc.identifier.issue2-
dc.identifier.spage418-
dc.identifier.epage438-
dc.identifier.eissn1537-2715-

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