Unconditional empirical likelihood approach for analytic use of public survey data
提出一种无条件经验似然方法，用于在公开调查数据中设计变量缺失时仍能进行一致估计和假设检验，适用于复杂多阶段抽样且无需已知包含概率或辅助变量。
Abstract Modeling survey data often requires having the knowledge of design and weighting variables. With public‐use survey data, some of these variables may not be available for confidentiality reasons. The proposed approach can be used in this situation, as long as calibrated weights and variables specifying the strata and primary sampling units are available. It gives consistent point estimation and a pivotal statistics for testing and confidence intervals. The proposed approach does not rely on with‐replacement sampling, single‐stage, negligible sampling fractions, or noninformative sampling. Adjustments based on design effects, eigenvalues, joint‐inclusion probabilities or bootstrap, are not needed. The inclusion probabilities and auxiliary variables do not have to be known. Multistage designs with unequal selection of primary sampling units are considered. Nonresponse can be easily accommodated if the calibrated weights include reweighting adjustment for nonresponse. We use an unconditional approach, where the variables and sample are random variables. The design can be informative.