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An Introduction to Model-Based Survey Sampling with Applications$
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Ray Chambers and Robert Clark

Print publication date: 2012

Print ISBN-13: 9780198566625

Published to Oxford Scholarship Online: May 2012

DOI: 10.1093/acprof:oso/9780198566625.001.0001

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Robust Estimation of the Prediction Variance

Robust Estimation of the Prediction Variance

Chapter:
(p.101) 9 Robust Estimation of the Prediction Variance
Source:
An Introduction to Model-Based Survey Sampling with Applications
Author(s):

Raymond L. Chambers

Robert G. Clark

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198566625.003.0009

Robust estimation of the prediction variance discusses the issues that arise when model misspecification is second order. That is, when the second order moments of the working model for the population are incorrect, as is typically the case. Here balanced sampling is of no avail, and alternative, more robust, methods of prediction variance must be used. This chapter focuses on development of these methods for the case where the working population model is the ratio model, as well as when a general linear predictor is used and the working model has quite general first and second order moments. The case of a clustered population with unknown within cluster heteroskedasticity is also discussed and the ultimate cluster variance estimator derived.

Keywords:   heteroskedasticity-robust variance estimation, ratio estimator, linear estimator, clustered data, ultimate cluster variance estimator

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