  In this paper, we propose a new statistical inference method for massive data
sets, which is very simple and efficient by combining divide-and-conquer method
and empirical likelihood. Compared with two popular methods (the bag of little
bootstrap and the subsampled double bootstrap), we make full use of data sets,
and reduce the computation burden. Extensive numerical studies and real data
analysis demonstrate the effectiveness and flexibility of our proposed method.
Furthermore, the asymptotic property of our method is derived.
