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In the past several years a wide range of methods for the construction ofregression trees and other estimators based on the recursive partitioning ofsamples have appeared in the statistics literature. Many applications involvedata collected through a complex sample design. At present, however, relativelylittle is known regarding the properties of these methods under complexdesigns. This article proposes a method for incorporating information about thecomplex sample design when building a regression tree using a recursivepartitioning algorithm. Sufficient conditions are established for asymptoticdesign L 2 consistency of these regression trees as estimators for an arbitraryregression function. The proposed method is illustrated with OccupationalEmployment Statistics establishment survey data linked to Quarterly Census ofEmployment and Wage payroll data of the Bureau of Labor Statistics. Performanceof the nonparametric estimator is investigated through a simulation study basedon this example.

Building Consistent Regression Trees From Complex Sample Data