Statistical inference in massive datasets by empirical likelihood

• URL: http://arxiv.org/abs/2004.08580v1
• Date: Sat, 18 Apr 2020 10:18:07 GMT
• Title: Statistical inference in massive datasets by empirical likelihood
• Authors: Xuejun Ma, Shaochen Wang, Wang Zhou
• Abstract summary: We propose a new statistical inference method for massive data sets. Our method is very simple and efficient by combining divide-and-conquer method and empirical likelihood.
• Score: 1.6887485428725042
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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