  Auxiliary information can increase the efficiency of survey estimators
through an assisting model when the model captures some of the relationship
between the auxiliary data and the study variables. Despite their superior
properties, model-assisted estimators are rarely used in anything but their
simplest form by statistical agencies to produce official statistics. This is
due to the fact that the more complicated models that have been used in
model-assisted estimation are often ill suited to the available auxiliary data.
Under a model-assisted framework, we propose a regression tree estimator for a
finite population total. Regression tree models are adept at handling the type
of auxiliary data usually available in the sampling frame and provide a model
that is easy to explain and justify. The estimator can be viewed as a
post-stratification estimator where the post-strata are automatically selected
by the recursive partitioning algorithm of the regression tree. We establish
consistency of the regression tree estimator and compare its performance to
other survey estimators using the US Bureau of Labor Statistics Occupational
Employment Statistics Survey.
