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"Robust regression using probabilistically linked data" by Ray L. Chambers, Enrico Fabrizi et al.

There is growing interest in a data integration approach to survey sampling, particularly where population registers are linked for sampling and subsequent analysis. The reason for doing this is simple: it is only by linking the same individuals in the different sources that it becomes possible to create a data set suitable for analysis. But data linkage is not error free. Many linkages are nondeterministic, based on how likely a linking decision corresponds to a correct match, that is, it brings together the same individual in all sources. High quality linking will ensure that the probability of this happening is high. Analysis of the linked data should take account of this additional source of error when this is not the case. This is especially true for secondary analysis carried out without access to the linking information, that is, the often confidential data that agencies use in their record matching. We describe an inferential framework that allows for linkage errors when sampli ....

Computational Methods , Maximum Likelihood Methods Statistical Learning , Exploratory Methods , Data Sciences , Modeling Methods Statistical , Graphical Methods , Data Analysis , Exchangeable Linkage Error , Inite Population Inference , Linked Data , Robust Estimation ,

"Small area estimation with linked data" by N. Salvati, E. Fabrizi et al.

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 ,