Model-Based Small Area Estimation with Application to Unemployment Estimates

The problem of Small Area Estimation (SAE) is complex because of various information sources and insufficient data. In this paper, an approach for SAE is presented for decision-making at national, regional and local level. We propose an Empirical Best Linear Unbiased Predictor (EBLUP) as an estimator in order to combine several information sources to evaluate various indicators. First, we present the urban audit project and its environmental, social and economic indicators. Secondly, we propose an approach for decision making in order to estimate indicators. An application is used to validate the theoretical proposal. Finally, a decision support system is presented based on open-source environment.

• Hichem Omrani
• Philippe Gerber
• Patrick Bousch

• Small area estimation
• statistical method
• sampling
• empirical best linear unbiased predictor (EBLUP)
• decision-making.

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