Statistical Science

Model-Assisted Survey Estimation with Modern Prediction Techniques

F. Jay Breidt and Jean D. Opsomer

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Abstract

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.

Article information

Source
Statist. Sci., Volume 32, Number 2 (2017), 190-205.

Dates
First available in Project Euclid: 11 May 2017

Permanent link to this document
https://projecteuclid.org/euclid.ss/1494489811

Digital Object Identifier
doi:10.1214/16-STS589

Mathematical Reviews number (MathSciNet)
MR3648955

Zentralblatt MATH identifier
1381.62060

Keywords
Machine learning nonparametric regression nearest neighbors neural network regression trees survey asymptotics

Citation

Breidt, F. Jay; Opsomer, Jean D. Model-Assisted Survey Estimation with Modern Prediction Techniques. Statist. Sci. 32 (2017), no. 2, 190--205. doi:10.1214/16-STS589. https://projecteuclid.org/euclid.ss/1494489811


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