Weighting estimation under bipartite incidence graph sampling

Abstract

AbstractBipartite 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.