Bipartite Incidence Graph (BIG) Sampling
Bipartite Incidence Graph (BIG) Sampling
Graph sampling is a statistical approach to study real graphs, which represent the structure of many technological, social or biological phenomena of interest. We develop bipartite incident graph sampling (BIGS) as a feasible representation of graph sampling from arbitrary finite graphs. It provides also a unified treatment of the existing unconventional sampling methods which were studied separately in the past, including indirect, network and adaptive cluster sampling. The sufficient and necessary conditions of feasible BIGS representation are established, given which one can apply a family of Hansen-Hurwitz type design-unbiased estimators in addition to the standard Horvitz-Thompson estimator. The approach increases therefore the potentials of efficiency gains in graph sampling. A general result regarding the relative efficiency of the two types of estimators is obtained. Numerical examples are given to illustrate the versatility of the proposed approach.
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Oguz Alper, Melike
02d5ed8a-e9e3-438a-95c0-709acd83a5f8
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Oguz Alper, Melike
02d5ed8a-e9e3-438a-95c0-709acd83a5f8
Zhang, Li-Chun and Oguz Alper, Melike
(2020)
Bipartite Incidence Graph (BIG) Sampling
38pp.
(Submitted)
Record type:
Monograph
(Working Paper)
Abstract
Graph sampling is a statistical approach to study real graphs, which represent the structure of many technological, social or biological phenomena of interest. We develop bipartite incident graph sampling (BIGS) as a feasible representation of graph sampling from arbitrary finite graphs. It provides also a unified treatment of the existing unconventional sampling methods which were studied separately in the past, including indirect, network and adaptive cluster sampling. The sufficient and necessary conditions of feasible BIGS representation are established, given which one can apply a family of Hansen-Hurwitz type design-unbiased estimators in addition to the standard Horvitz-Thompson estimator. The approach increases therefore the potentials of efficiency gains in graph sampling. A general result regarding the relative efficiency of the two types of estimators is obtained. Numerical examples are given to illustrate the versatility of the proposed approach.
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Submitted date: 20 March 2020
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Local EPrints ID: 473405
URI: http://eprints.soton.ac.uk/id/eprint/473405
PURE UUID: 522f8cf0-ad0d-4200-a9d6-07403e3c6890
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Date deposited: 17 Jan 2023 17:48
Last modified: 09 Feb 2023 03:12
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Author:
Melike Oguz Alper
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