文章基本信息

标题：Empirical likelihood inference with public-use survey data
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作者：Puying Zhao ; J. N. K. Rao ; Changbao Wu 等
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2020
卷号：14
期号：1
页码：2484-2509
DOI：10.1214/20-EJS1726
语种：English
出版社：Institute of Mathematical Statistics
摘要：Public-use survey data are an important source of information for researchers in social sciences and health studies to build statistical models and make inferences on the target finite population. This paper presents two general inferential tools through the pseudo empirical likelihood and the sample empirical likelihood methods. Theoretical results on point estimation and linear or nonlinear hypothesis tests involving parameters defined through estimating equations are established, and practical issues with the implementation of the proposed methods are discussed. Results from simulation studies and an application to the 2016 General Social Survey dataset of Statistics Canada show that the proposed methods work well under different scenarios. The inferential procedures and theoretical results presented in the paper make the empirical likelihood a practically useful tool for users of complex survey data.
关键词：Auxiliary information; bootstrap; calibration weighting; design-based inference; estimating equations; hypothesis test; replication weights; survey design; variable selection