Statistical inference in massive datasets by empirical likelihood
This addresses computational challenges in statistical inference for massive datasets, though it appears incremental as an improvement over existing divide-and-conquer and bootstrap methods.
The authors tackled the problem of statistical inference in massive datasets by proposing a new method that combines divide-and-conquer with empirical likelihood, which is simpler and more efficient than existing approaches like bag of little bootstrap and subsampled double bootstrap, reducing computational burden while fully utilizing data.
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.