Size and shape analysis of error-prone shape data

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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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Bibliographic Details
Main Authors:	Du, J., Dryden, Ian L., Huang, X.
Format:	Article
Published:	Taylor & Francis 2015
Subjects:	Complex normal; Configuration; Landmark; Ordinary Procrustes analysis; Quaternion
Online Access:	https://eprints.nottingham.ac.uk/41096/

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