Small area estimation with linked data
| Autor: | Enrico Fabrizi, Ray Chambers, Maria Giovanna Ranalli, Nicola Salvati |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
Linked data exchangeable linkage error finite population inference linear mixed models mean squared error estimation robust estimation robust estimation exchangeable linkage error Generalized linear mixed model linear mixed models Small area estimation Settore SECS-S/01 - STATISTICA Statistics finite population inference mean squared error estimation Statistics Probability and Uncertainty Mathematics |
| Popis: | 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 from the European Survey on Income and Living Conditions and the Italian integrated archive of economic and demographic micro data in order to obtain estimates of the average equivalised income for labour market areas in central Italy. |
| Databáze: | OpenAIRE |
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