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- W3015582110 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 linear estimators based on the edges in the sample bipartite incidence graph, 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 and Sirken (1965). Our generalisation allows one to devise other unbiased estimators, thereby providing a potential of efficiency gains in applications. Illustrations are given for adaptive cluster sampling, line-intercept sampling and simulated graphs." @default.
- W3015582110 created "2020-04-17" @default.
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- W3015582110 date "2020-04-08" @default.
- W3015582110 modified "2023-10-17" @default.
- W3015582110 title "Incidence weighting estimation under bipartite incidence graph sampling" @default.
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- W3015582110 doi "https://doi.org/10.48550/arxiv.2004.04257" @default.
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