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

TL;DR

This paper introduces a regression tree estimator for survey data that automatically selects post-strata, improving efficiency and interpretability compared to traditional methods, and demonstrates its consistency and performance with US labor statistics.

Contribution

It proposes a novel model-assisted regression tree estimator that automates post-stratum selection, addressing limitations of traditional models in survey estimation.

Findings

Estimator is consistent under certain conditions.

Performance surpasses traditional estimators in simulations.

Automatically selects meaningful post-strata using recursive partitioning.

Abstract

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.