基于经验似然的分布估计

Pub Date : 2022-06-21 DOI:10.1002/cjs.11706
Qianqian Liu, Zhouping Li
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引用次数: 1

摘要

随着科学技术的发展,存储在多台机器中的海量数据集越来越普遍。众所周知,由于计算时间过长、内存限制、通信成本和隐私问题,传统的统计方法可能不适用于分析大型数据集。本文为分布式计算环境开发了分治经验似然(DEL)和分治指数倾斜经验似然(DETEL)方法。我们研究了DEL和DETEL估计量的理论性质。特别地,我们导出了DEL和DETEL估计量的均方误差的上界,并且在一些温和的条件下,我们证明了所提出的估计量的一致性和渐近正态性。进行了仿真研究和实际数据分析,以证明所提出方法的有限样本性能。
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Distributed estimation with empirical likelihood

With the development of science and technology, massive datasets stored in multiple machines are increasingly prevalent. It is known that traditional statistical methods may be infeasible for analyzing large datasets owing to excessive computing time, memory limitations, communication costs, and privacy concerns. This article develops divide-and-conquer empirical likelihood (DEL) and divide-and-conquer exponentially tilted empirical likelihood (DETEL) methods for the distributed computing setting. We investigate the theoretical properties of the DEL and DETEL estimators. In particular, we derive upper bounds for the mean squared errors of the DEL and DETEL estimators, and, under some mild conditions, we prove the consistency and the asymptotic normality of the proposed estimators. Simulation studies and a real data analysis are carried out to demonstrate the finite-sample performance of the proposed methods.

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