Abstract
In Official Statistics, interest for data integration has been increasingly growing, though the effect of this procedure on the statistical analyses has been disregarded for a long time. In recent years, however, it is largely recognized that linkage is not an error-free procedure and linkage errors, as false links and missed links can invalidate standard estimates. More recently, growing attention is devoted to the effect of linkage errors on the subsequent analyses. For instance, Samart and Chambers (Samart in Aust N Z J Stat 56, 2014 [14]) consider the effect of linkage errors on mixed effect models. Their proposal finds a natural application in the context of longitudinal studies, where repeated measures are taken on the same individuals. In official statistics, the mixed models is largely exploited for small area estimation to increase detailed information at local level. In this work, an EBLUP estimator that takes account of the linkage errors is derived.
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Di Consiglio, L., Tuoto, T. (2017). Small Area Estimation in the Presence of Linkage Errors. In: Ferraro, M., et al. Soft Methods for Data Science. SMPS 2016. Advances in Intelligent Systems and Computing, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-42972-4_21
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DOI: https://doi.org/10.1007/978-3-319-42972-4_21
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