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2020 Empirical likelihood inference with public-use survey data
Puying Zhao, J. N. K. Rao, Changbao Wu
Electron. J. Statist. 14(1): 2484-2509 (2020). DOI: 10.1214/20-EJS1726


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.


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Puying Zhao. J. N. K. Rao. Changbao Wu. "Empirical likelihood inference with public-use survey data." Electron. J. Statist. 14 (1) 2484 - 2509, 2020.


Received: 1 March 2019; Published: 2020
First available in Project Euclid: 1 July 2020

zbMATH: 07235717
MathSciNet: MR4118335
Digital Object Identifier: 10.1214/20-EJS1726

Primary: 62D05
Secondary: 62G05 , 62G10

Keywords: Auxiliary information , bootstrap , calibration weighting , design-based inference , estimating equations , hypothesis test , replication weights , survey design , Variable selection


Vol.14 • No. 1 • 2020
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