Statistical inference in massive datasets by empirical likelihood
Methodology
2020-04-21 v1 Statistics Theory
Machine Learning
Statistics Theory
Abstract
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.
Cite
@article{arxiv.2004.08580,
title = {Statistical inference in massive datasets by empirical likelihood},
author = {Xuejun Ma and Shaochen Wang and Wang Zhou},
journal= {arXiv preprint arXiv:2004.08580},
year = {2020}
}
Comments
33 pages