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Auxiliary information can increase the efficiency of survey estimatorsthrough an assisting model when the model captures some of the relationshipbetween the auxiliary data and the study variables. Despite their superiorproperties, model-assisted estimators are rarely used in anything but theirsimplest form by statistical agencies to produce official statistics. This isdue to the fact that the more complicated models that have been used inmodel-assisted estimation are often ill suited to the available auxiliary data.Under a model-assisted framework, we propose a regression tree estimator for afinite population total. Regression tree models are adept at handling the typeof auxiliary data usually available in the sampling frame and provide a modelthat is easy to explain and justify. The estimator can be viewed as apost-stratification estimator where the post-strata are automatically selectedby the recursive partitioning algorithm of the regression tree. We establishconsistency of the regression tree estimator and compare its performance toother survey estimators using the US Bureau of Labor Statistics OccupationalEmployment Statistics Survey.

Automated Selection of Post-Strata using a Model-Assisted Regression Tree Estimator