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A jackknife variance estimator for self-weighted two-stage samples

Escobar, E.L. and Berger, Y.G. (2013) A jackknife variance estimator for self-weighted two-stage samples Statistica Sinica, 23, (2), pp. 1-19. (doi:10.5705/ss.2011.263).

Record type: Article


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.

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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


Local EPrints ID: 350426
ISSN: 1017-0405
PURE UUID: 2fb2bdbf-f7ef-4487-a6c2-5a2a35e36b53

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Date deposited: 04 Apr 2013 10:59
Last modified: 18 Jul 2017 04:34

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Author: E.L. Escobar
Author: Y.G. Berger

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