A jackknife variance estimator for unequal probability sampling
A jackknife variance estimator for unequal probability sampling
The jackknife method is often used for variance estimation in sample surveys but has only been developed for a limited class of sampling designs. We propose a jackknife variance estimator which is defined for any without-replacement unequal probability sampling design. We demonstrate design consistency of this estimator for a broad class of point estimators. A Monte Carlo study shows how the proposed estimator may improve on existing estimators.
inclusion probabilities, linearization, pseudovalues, smooth function of means, stratification
79-89
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Skinner, Chris J.
dec5ef40-49ef-492a-8a1d-eb8c6315b8ce
2005
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Skinner, Chris J.
dec5ef40-49ef-492a-8a1d-eb8c6315b8ce
Berger, Yves G. and Skinner, Chris J.
(2005)
A jackknife variance estimator for unequal probability sampling.
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67 (1), .
(doi:10.1111/j.1467-9868.2005.00489.x).
Abstract
The jackknife method is often used for variance estimation in sample surveys but has only been developed for a limited class of sampling designs. We propose a jackknife variance estimator which is defined for any without-replacement unequal probability sampling design. We demonstrate design consistency of this estimator for a broad class of point estimators. A Monte Carlo study shows how the proposed estimator may improve on existing estimators.
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Published date: 2005
Keywords:
inclusion probabilities, linearization, pseudovalues, smooth function of means, stratification
Identifiers
Local EPrints ID: 44058
URI: http://eprints.soton.ac.uk/id/eprint/44058
ISSN: 1369-7412
PURE UUID: 2fc201a5-66d8-4e1a-8c81-9cd5651f17fd
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Date deposited: 13 Feb 2007
Last modified: 16 Mar 2024 03:03
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Author:
Chris J. Skinner
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