Abstract: Small area estimation is important in survey analysis when domain (subpopulation) sample sizes are too small to provide adequate precision for direct domain estimators. Popular techniques for small area estimation use implicit or explicit statistical models to indirectly estimate the small area parameters of interest. Indirect estimation requires you to go beyond the survey data analysis methods that are available in the SAS/STAT® survey procedures. This paper describes the use of the MIXED, IML, and MCMC procedures to fit unit-level and area-level models, and to obtain small area predictions and the mean squared error of predictions. Hierarchical Bayes models are also discussed as extensions to the basic models.
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