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含测量误差形状数据的大小与形状分析AI 解读

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…

结构测量误差模型中的潜变量模型稳健性AI 解读

We present methods for diagnosing the effects of model misspecification of the true-predictor distribution in structural measurement error models. We first formulate latent-model robustness theoretically. Then we provide practical techniques for examining the adequacy of an assumed latent predictor model. The methods are illustrated via analytical examples, application to simulated data and with data from a study of coronary heart disease.