Premium
Some Theoretical and Practical Aspects of Empirical Likelihood Methods for Complex Surveys
Author(s) -
Zhao Puying,
Wu Changbao
Publication year - 2019
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/insr.12291
Subject(s) - inference , computer science , sample (material) , sample size determination , empirical likelihood , sampling (signal processing) , point estimation , survey sampling , model selection , statistical inference , population , mathematics , statistics , machine learning , artificial intelligence , chemistry , demography , filter (signal processing) , chromatography , sociology , computer vision
Summary This paper provides an overview on two parallel approaches to design‐based inference with complex survey data: the pseudo empirical likelihood methods and the sample empirical likelihood methods. The general framework covers parameters defined through smooth or non‐differentiable estimating functions for analytic use of survey data as well as descriptive finite population parameters, and the theory focuses on point estimation, hypothesis tests and variable selection under an arbitrary sampling design. Major practical issues for the implementation of the methods, including computational algorithms, are briefly discussed. Results from simulation studies to compare the finite sample performances of the two approaches are presented.