Influence Assessment in an Heteroscedastic Errors-in-Variables Model
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Date
2012
Journal Title
Journal ISSN
Volume Title
Publisher
TAYLOR & FRANCIS INC
Abstract
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.
Description
Keywords
Case deletion, EM algorithm, Equation-error models, Errors-in-variables models, Linear mixed models, Local influence, Incomplete-Data, Regression