Size and Shape Analysis of Error-Prone Shape Data

We consider the problem of comparing sizes and shapes of objects when landmark data are prone to measurement error. We show that naive implementation of ordinary Procrustes analysis that ignores measurement error can compromise inference. To account for measurement error, we propose the conditional...

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Detalles Bibliográficos
Autores principales: Du, Jiejun, Dryden, Ian L., Huang, Xianzheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2015
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4456951/
https://www.ncbi.nlm.nih.gov/pubmed/26109745
http://dx.doi.org/10.1080/01621459.2014.908779

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