Open Access
May 2017 Model-Assisted Survey Estimation with Modern Prediction Techniques
F. Jay Breidt, Jean D. Opsomer
Statist. Sci. 32(2): 190-205 (May 2017). DOI: 10.1214/16-STS589


This paper reviews the design-based, model-assisted approach to using data from a complex survey together with auxiliary information to estimate finite population parameters. A general recipe for deriving model-assisted estimators is presented and design-based asymptotic analysis for such estimators is reviewed. The recipe allows for a very broad class of prediction methods, with examples from the literature including linear models, linear mixed models, nonparametric regression and machine learning techniques.


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F. Jay Breidt. Jean D. Opsomer. "Model-Assisted Survey Estimation with Modern Prediction Techniques." Statist. Sci. 32 (2) 190 - 205, May 2017.


Published: May 2017
First available in Project Euclid: 11 May 2017

zbMATH: 1381.62060
MathSciNet: MR3648955
Digital Object Identifier: 10.1214/16-STS589

Keywords: machine learning , nearest neighbors , neural network , Nonparametric regression , regression trees , survey asymptotics

Rights: Copyright © 2017 Institute of Mathematical Statistics

Vol.32 • No. 2 • May 2017
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