  Public-use survey data are an important source of information for researchers
in social science 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.
