Statistical inference in massive datasets by empirical likelihood

Xuejun Ma (), Shaochen Wang () and Wang Zhou ()
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Xuejun Ma: Soochow University
Shaochen Wang: South China University of Technology
Wang Zhou: National University of Singapore

Computational Statistics, 2022, vol. 37, issue 3, No 6, 1143-1164

Abstract: 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.

Keywords: Bootstrap; Divide-and-conquer; Hypothesis test; Empirical likelihood (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-021-01153-9

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