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Size and Shape Analysis of Error-Prone Shape Data

Author

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  • Jiejun Du
  • Ian L. Dryden
  • Xianzheng Huang

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.

Suggested Citation

  • Jiejun Du & Ian L. Dryden & Xianzheng Huang, 2015. "Size and Shape Analysis of Error-Prone Shape Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 368-379, March.
    • Handle: RePEc:taf:jnlasa:v:110:y:2015:i:509:p:368-379
    DOI: 10.1080/01621459.2014.908779
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    Cited by:

    1. Irene Epifanio & María Victoria Ibáñez & Amelia Simó, 2018. "Archetypal shapes based on landmarks and extension to handle missing data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 705-735, September.

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