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Abstract
Data linkage can be used to combine values of the variable of interest from a national survey with values of auxiliary variables obtained from another source, such as a population register, for use in small area estimation. However, linkage errors can induce bias when fitting regression models; moreover, they can create non-representative outliers in the linked data in addition to the presence of potential representative outliers. In this paper, we adopt a secondary analyst’s point of view, assuming that limited information is available on the linkage process, and develop small area estimators based on linear mixed models and M-quantile models to accommodate linked data containing a mix of both types of outliers. We illustrate the properties of these small area estimators, as well as estimators of their mean squared error, by means of model-based and design-based simulation experiments. We further illustrate the proposed methodology by applying it to linked data fro ....

European Survey On Income , European Survey , Living Conditions , Exchangeable Linkage Error , Inite Population Inference , Linear Mixed Models , Ean Squared Error Estimation , Robust Estimation , ஐரோப்பிய கணக்கெடுப்பு , வாழும் நிபந்தனைகள் ,