Size and Shape Analysis of Error-Prone Shape Data
研究了地标数据存在测量误差时如何比较物体的大小和形状，发现忽略测量误差的普通Procrustes分析会损害推断，并提出了条件得分方法以保证一致推断。
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.