Weighting estimation under bipartite incidence graph sampling
Weighting estimation under bipartite incidence graph sampling
Bipartite incidence graph sampling provides a unified representation of many sampling situations for the purpose of estimation, including the existing unconventional sampling methods, such as indirect, network or adaptive cluster sampling, which are not originally described as graph problems. We develop a large class of design-based linear estimators, defined for the sample edges and subjected to a general condition of design unbiasedness. The class contains as special cases the classic Horvitz-Thompson estimator, as well as the other unbiased estimators in the literature of unconventional sampling, which can be traced back to Birnbaum et al. (1965). Our generalisation allows one to devise other unbiased estimators in future, thereby providing a potential of efficiency gains. Illustrations are given for adaptive cluster sampling, line-intercept sampling and simulated graphs.
Rao-Blackwellisation, graph sampling, multiplicity estimator
Patone, Martina
51bbd4cc-1c19-4a64-a0c2-1534b076fa79
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
25 September 2022
Patone, Martina
51bbd4cc-1c19-4a64-a0c2-1534b076fa79
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Patone, Martina and Zhang, Li-Chun
(2022)
Weighting estimation under bipartite incidence graph sampling.
Statistical Methods & Applications.
(doi:10.1007/s10260-022-00659-w).
Abstract
Bipartite incidence graph sampling provides a unified representation of many sampling situations for the purpose of estimation, including the existing unconventional sampling methods, such as indirect, network or adaptive cluster sampling, which are not originally described as graph problems. We develop a large class of design-based linear estimators, defined for the sample edges and subjected to a general condition of design unbiasedness. The class contains as special cases the classic Horvitz-Thompson estimator, as well as the other unbiased estimators in the literature of unconventional sampling, which can be traced back to Birnbaum et al. (1965). Our generalisation allows one to devise other unbiased estimators in future, thereby providing a potential of efficiency gains. Illustrations are given for adaptive cluster sampling, line-intercept sampling and simulated graphs.
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SMAP-r2
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s10260-022-00659-w
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Accepted/In Press date: 20 September 2022
Published date: 25 September 2022
Keywords:
Rao-Blackwellisation, graph sampling, multiplicity estimator
Identifiers
Local EPrints ID: 470762
URI: http://eprints.soton.ac.uk/id/eprint/470762
ISSN: 1618-2510
PURE UUID: a9232ebc-23f1-40f3-a3a8-f1dbbe274116
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Date deposited: 19 Oct 2022 16:59
Last modified: 17 Mar 2024 03:30
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Author:
Martina Patone
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