  In this paper, we propose a novel approach for tackling the obstacles of
empirical likelihood in the face of massive data, which is called split sample
mean empirical likelihood (SSMEL), our approach provides a unique perspective
for solving big data problems. We show that the SSMEL estimator has the same
estimation efficiency as the empirical likelihood estimator with the full
dataset, and maintains the important statistical property of Wilks' theorem,
allowing our proposed approach to be used for statistical inference without
estimating the covariance matrix. This effectively tackles the hurdle of the
Divide and Conquer (DC) algorithm for statistical inference. We further
illustrate the proposed approach via simulation studies and real data analysis.
