Size and Shape Analysis of Error-Prone Shape Data

Jiejun Du, Ian L. Dryden and Xianzheng Huang

Journal of the American Statistical Association, 2015, vol. 110, issue 509, 368-379

Abstract: 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. Supplementary materials for this article are available online.

Date: 2015
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DOI: 10.1080/01621459.2014.908779

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