This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 143
Type: Contributed
Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Survey Research Methods
Abstract - #307942
Title: Building Regression Trees on Data from a Complex Sample
Author(s): Daniell Toth*+ and John L. Eltinge
Companies: Bureau of Labor Statistics and Bureau of Labor Statistics
Address: 2 Massachusetts Ave, N.E., Washington, DC, 20212,
Keywords: analysis of survey data ; asymptotic consistency ; nonparametric regression ; ; Occupation and Employment Survey ; recursive partitioning ; superpopulation model
Abstract:

In the past several years the statistical literature has developed a wide range of methods for the construction of regression trees and other estimators based on the recursive partitioning of a sample. Many prospective 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 paper establishes sufficient conditions to guarantee asymptotic design consistency of regression trees as an estimator for the conditional mean of the population. Conditions on the population distribution and survey design, for which the results are proved, are quite general. Performance of this proposed nonparametric estimator is investigated through a simulation study based on wage data from the Occupation and Employment Survey (OES) of the Bureau of Labor Statistics (BLS).


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