On the two-phase framework for joint model and design-based inference
本文建立了一个数学框架，将有限总体抽样理论与无限总体抽样理论统一，验证了Hartley和Sielken提出的两阶段“超总体观点”，并证明了样本估计量和模型统计量的渐近性质。
We establish a mathematical framework that formally validates the two-phase “super-population viewpoint” proposed by Hartley and Sielken [Biometrics 31 (1975) 411–422] by defining a product probability space which includes both the design space and the model space. The methodology we develop combines finite population sampling theory and the classical theory of infinite population sampling to account for the underlying processes that produce the data under a unified approach. Our key results are the following: first, if the sample estimators converge in the design law and the model statistics converge in the model, then, under certain conditions, they are asymptotically independent, and they converge jointly in the product space; second, the sample estimating equation estimator is asymptotically normal around a super-population parameter.