A jackknife variance estimator for self-weighted two-stage samples
A jackknife variance estimator for self-weighted two-stage samples
Self-weighted two-stage sampling designs are popular in practice as they simplify field-work. It is common in practice to compute variance estimates only from the first sampling stage, neglecting the second stage. This omission may induce a bias in variance estimation; especially in situations where there is low variability between clusters or when sampling fractions are non-negligible. We propose a design-consistent jackknife variance estimator that takes account of all stages via deletion of clusters and observations within clusters. The proposed jackknife can be used for a wide class of point estimators. It does not need joint-inclusion probabilities and naturally includes finite population corrections. A simulation study shows that the proposed estimator can be more accurate than standard jackknifes (Rao, Wu, and Yue (1992)) for self-weighted two-stage sampling designs.
linearisation, pseudovalues, sen-yates-grundy form, smooth function of means, stratification
1-19
Escobar, E.L.
b2431dc1-bbfb-4f94-bbae-3877cd1de7d9
Berger, Y.G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
April 2013
Escobar, E.L.
b2431dc1-bbfb-4f94-bbae-3877cd1de7d9
Berger, Y.G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Escobar, E.L. and Berger, Y.G.
(2013)
A jackknife variance estimator for self-weighted two-stage samples.
Statistica Sinica, 23 (2), .
(doi:10.5705/ss.2011.263).
Abstract
Self-weighted two-stage sampling designs are popular in practice as they simplify field-work. It is common in practice to compute variance estimates only from the first sampling stage, neglecting the second stage. This omission may induce a bias in variance estimation; especially in situations where there is low variability between clusters or when sampling fractions are non-negligible. We propose a design-consistent jackknife variance estimator that takes account of all stages via deletion of clusters and observations within clusters. The proposed jackknife can be used for a wide class of point estimators. It does not need joint-inclusion probabilities and naturally includes finite population corrections. A simulation study shows that the proposed estimator can be more accurate than standard jackknifes (Rao, Wu, and Yue (1992)) for self-weighted two-stage sampling designs.
Text
Escobar_Berger_2013_Sinica.pdf
- Accepted Manuscript
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Published date: April 2013
Keywords:
linearisation, pseudovalues, sen-yates-grundy form, smooth function of means, stratification
Organisations:
Statistical Sciences Research Institute
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Local EPrints ID: 350426
URI: http://eprints.soton.ac.uk/id/eprint/350426
ISSN: 1017-0405
PURE UUID: 2fb2bdbf-f7ef-4487-a6c2-5a2a35e36b53
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Date deposited: 04 Apr 2013 10:59
Last modified: 15 Mar 2024 03:00
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Author:
E.L. Escobar
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