This paper presents a statistical methodology for influence assessment in heteroscedastic errors-in-variables models, illustrated using cardiovascular disease data.
May refine influence diagnostics in CVD models; leaves open prospective validation before clinical use.
The main goal of this article is to consider influence assessment in models with error-prone observations and variances of the measurement errors changing across observations. The techniques enable to identify potential influential elements and also to quantify the effects of perturbations in these elements on some results of interest. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease.
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Galea et al. (2012) studied this question.
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