论文标题
人群级别信息合并了复杂调查数据集的参数估计
Population level information combined parameter estimation from complex survey datasets
论文作者
论文摘要
我们考虑了一个基于内容丰富的抽样设计和人群级信息的统计模型推断的经验可能性框架。人口级信息以估计方程的形式汇总，并通过其他约束将其纳入推理。协变信息通过权重和估计方程均包含。估计器基于条件权重。我们表明，在常规条件下，随着人口规模增加无限，这些估计值非常一致，无偏见且正态分布。此外，它们比其他概率加权类似物更有效。我们的框架在条件经验可能性方面为反概率加权分数估计量提供了额外的理由。我们通过将出生注册数据与面板调查数据相结合以估算年度第一出生概率来介绍人口危害建模。
We consider an empirical likelihood framework for inference for a statistical model based on an informative sampling design and population-level information. The population-level information is summarized in the form of estimating equations and incorporated into the inference through additional constraints. Covariate information is incorporated both through the weights and the estimating equations. The estimator is based on conditional weights. We show that under usual conditions, with population size increasing unbounded, the estimates are strongly consistent, asymptotically unbiased, and normally distributed. Moreover, they are more efficient than other probability-weighted analogs. Our framework provides additional justification for inverse probability weighted score estimators in terms of conditional empirical likelihood. We give an application to demographic hazard modeling by combining birth registration data with panel survey data to estimate annual first birth probabilities.
