二部关联图抽样下的关联权估计
Incidence weighting estimation under bipartite incidence graph sampling
摘要：
二部关联图抽样为估计的目的提供了许多抽样情况的统一表示，包括现有的非常规抽样方法，如间接，网络或自适应整群抽样，这些方法最初并不被描述为图问题。我们开发了一个大类的线性估计的样本二部关联图的边缘的基础上，设计无偏性的一般条件。这个类包含经典的Horvitz-Thompson估计量作为特例，以及非常规抽样文献中的其他无偏估计量，这些估计量可以追溯到Birnbaum和Sirken（1965）。我们的推广允许设计其他无偏估计，从而提供了一个潜在的应用程序中的效率增益。文中给出了自适应整群抽样、截线抽样和模拟图的例子。
英文摘要：
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.
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