Robust case-weighting for multipurpose establishment surveys.
Robust case-weighting for multipurpose establishment surveys.
Case-weighting or assigning a unique weight to each sample unit is a popular method of sample weighting when internal consistency of the survey estimates is paramount. If in addition external constraints on key variables (the survey benchmarks) must also be met, then case-weights computed via generalised least squares, based on an assumed linear regression model for the survey variables, can be used. Unfortunately, this method of weighting can lead to negative case-weights. It is also susceptible to bias if the linear model is misspecified. This article proposes a modified method of linear regression-based case-weighting which ensures positive weights via use of a ridging procedure, and model misspecification robustness via the inclusion of a nonparametric regression bias correction factor. Empirical results which illustrate the gains from the new method of weighting are presented
sample surveys, sample weighting, model-based approach, ridge regression, nonparametric regression
3-32
Chambers, R.L.
df4b494f-3260-4198-8137-3bf1d9c60fa2
March 1996
Chambers, R.L.
df4b494f-3260-4198-8137-3bf1d9c60fa2
Chambers, R.L.
(1996)
Robust case-weighting for multipurpose establishment surveys.
Journal of Official Statistics, 12 (1), .
Abstract
Case-weighting or assigning a unique weight to each sample unit is a popular method of sample weighting when internal consistency of the survey estimates is paramount. If in addition external constraints on key variables (the survey benchmarks) must also be met, then case-weights computed via generalised least squares, based on an assumed linear regression model for the survey variables, can be used. Unfortunately, this method of weighting can lead to negative case-weights. It is also susceptible to bias if the linear model is misspecified. This article proposes a modified method of linear regression-based case-weighting which ensures positive weights via use of a ridging procedure, and model misspecification robustness via the inclusion of a nonparametric regression bias correction factor. Empirical results which illustrate the gains from the new method of weighting are presented
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Published date: March 1996
Keywords:
sample surveys, sample weighting, model-based approach, ridge regression, nonparametric regression
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Local EPrints ID: 34175
URI: http://eprints.soton.ac.uk/id/eprint/34175
ISSN: 0282-423X
PURE UUID: b07ebeff-32e6-41d9-ae19-58db83eb7f7e
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Date deposited: 19 Dec 2007
Last modified: 11 Dec 2021 15:23
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Author:
R.L. Chambers
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