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Size and shape analysis of error-prone shape data. (English) Zbl 1373.62127

Summary: 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 score method for matching configurations, which guarantees consistent inference under mild model assumptions. The effects of measurement error on inference from naive Procrustes analysis and the performance of the proposed method are illustrated via simulation and application in three real data examples.

MSC:

62G05 Nonparametric estimation
57N25 Shapes (aspects of topological manifolds)
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