# Building Consistent Regression Trees From Complex Sample Data

arXiv: https://arxiv.org/abs/1407.1079 · HTML: https://mlsanity.com/paper/1407.1079/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2014.

## Built on by (most cited first, 4 of 4)

- [Generalization Bounds and Stopping Rules for Learning with Self-Selected Data](https://mlsanity.com/paper/2505.07367/) (2025)
- [Building Trees for Probabilistic Prediction via Scoring Rules](https://mlsanity.com/paper/2402.11052/) (2024)
- [Consistency of the k-Nearest Neighbor Regressor under Complex Survey Designs](https://mlsanity.com/paper/2603.17551/) (2026)
- [Survey-aware Machine Learning: A Guideline for Valid Population Health Inference based on Scoping Review](https://mlsanity.com/paper/2605.08963/) (2026)

## Abstract (arXiv)

In the past several years a wide range of methods for the construction of regression trees and other estimators based on the recursive partitioning of samples have appeared in the statistics literature. Many applications involve data collected through a complex sample design. At present, however, relatively little is known regarding the properties of these methods under complex designs. This article proposes a method for incorporating information about the complex sample design when building a regression tree using a recursive partitioning algorithm. Sufficient conditions are established for asymptotic design L 2 consistency of these regression trees as estimators for an arbitrary regression function. The proposed method is illustrated with Occupational Employment Statistics establishment survey data linked to Quarterly Census of Employment and Wage payroll data of the Bureau of Labor Statistics. Performance of the nonparametric estimator is investigated through a simulation study based on this example.