Building Consistent Regression Trees From Complex Sample Data

• 成果类型: Article
• 署名作者: Toth, Daniell; Eltinge, John L.
• 署名单位: United States Department of Labor
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2011.tm10383
• 发表日期: 2011
• 页码: 1626-1636
• 关键词: Survey design; CLASSIFICATION

摘要

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.