论文标题
在两分的发射率图中的发病加权估计
Incidence weighting estimation under bipartite incidence graph sampling
论文作者
论文摘要
双方入射图采样提供了许多采样情况的统一表示，以进行估计，包括现有的非常规采样方法，例如间接，网络或自适应群集采样，最初并非被描述为图形问题。我们根据样品两分入射图中的边缘开发了一大批线性估计器，并具有设计无偏的一般条件。该类包含特殊情况，是经典的Horvitz-Thompson估计量，以及非常规抽样文献中的其他无偏估计器，可以追溯到Birnbaum和Sirken（1965）。我们的概括使人们可以设计其他公正的估计器，从而在应用中提供了效率提高的潜力。给出了用于自适应群集采样，线截距采样和模拟图的插图。
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.
