Finite Population Sampling With Multivariate Auxiliary Information
本文研究在大样本调查和回归超总体模型下，近似设计无偏且接近最优的抽样策略，提出新预测器类，并证明广义回归预测器在渐近设计无偏预测器中普遍存在，结合模型分层构建高效策略。
Abstract This article examines strategies that are approximately design-unbiased and nearly optimal, assuming a large-sample survey and a regression superpopulation model. A new class of predictors is proposed to link certain features of optimal design-unbiased and model-unbiased predictors. Generalized regression predictors are shown to pervade the subclass of asymptotically design-unbiased (ADU) predictors. Generalized regression predictors are combined with model-based stratification to construct highly efficient ADU strategies.