论文标题
通过经验可能性推断大规模数据集的统计推断
Statistical inference in massive datasets by empirical likelihood
论文作者
论文摘要
在本文中，我们为大规模数据集提出了一种新的统计推断方法，这是通过将分裂和纠纷方法和经验可能性相结合而非常简单有效的。与两种流行的方法相比，我们充分利用数据集并减轻计算负担。广泛的数值研究和实际数据分析证明了我们提出的方法的有效性和灵活性。此外，我们方法的渐近特性得出了。
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.
