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...
| Main Authors: | , , |
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| Format: | Article |
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Taylor & Francis
2015
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| Online Access: | https://eprints.nottingham.ac.uk/41096/ |
| _version_ | 1848796195211706368 |
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| author | Du, J. Dryden, Ian L. Huang, X. |
| author_facet | Du, J. Dryden, Ian L. Huang, X. |
| author_sort | Du, J. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:44:07Z |
| format | Article |
| id | nottingham-41096 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:44:07Z |
| publishDate | 2015 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-410962020-05-04T20:09:31Z https://eprints.nottingham.ac.uk/41096/ Size and shape analysis of error-prone shape data Du, J. Dryden, Ian L. Huang, X. 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. Taylor & Francis 2015-03 Article PeerReviewed Du, J., Dryden, Ian L. and Huang, X. (2015) Size and shape analysis of error-prone shape data. Journal of the American Statistical Association, 110 (509). pp. 368-377. ISSN 1537-274X Complex normal; Configuration; Landmark; Ordinary Procrustes analysis; Quaternion http://www.tandfonline.com/doi/full/10.1080/01621459.2014.908779 doi:10.1080/01621459.2014.908779 doi:10.1080/01621459.2014.908779 |
| spellingShingle | Complex normal; Configuration; Landmark; Ordinary Procrustes analysis; Quaternion Du, J. Dryden, Ian L. Huang, X. Size and shape analysis of error-prone shape data |
| title | Size and shape analysis of error-prone shape data |
| title_full | Size and shape analysis of error-prone shape data |
| title_fullStr | Size and shape analysis of error-prone shape data |
| title_full_unstemmed | Size and shape analysis of error-prone shape data |
| title_short | Size and shape analysis of error-prone shape data |
| title_sort | size and shape analysis of error-prone shape data |
| topic | Complex normal; Configuration; Landmark; Ordinary Procrustes analysis; Quaternion |
| url | https://eprints.nottingham.ac.uk/41096/ https://eprints.nottingham.ac.uk/41096/ https://eprints.nottingham.ac.uk/41096/ |