 Communication-efficient distributed estimator for generalized linear models with a diverging number of covariatesPing Zhou, Zhen Yu, Jingyi Ma, Maozai Tian and Ye Fan Computational Statistics & Data Analysis, 2021, vol. 157, issue C Abstract: Nowadays, it has become increasingly common to store large-scale data sets distributedly across a great number of clients. The aim of the study is to develop a distributed estimator for generalized linear models (GLMs) in the “large n, diverging pn” framework with a weak assumption on the number of clients. When the dimension diverges at the rate of o(n), the asymptotic efficiency of the global maximum likelihood estimator (MLE), the one-step MLE, and the aggregated estimating equation (AEE) estimator for GLMs are established. A novel distributed estimator is then proposed with two rounds of communication. It has the same asymptotic efficiency as the global MLE under pn=o(n). The assumption on the number of clients is more relaxed than that of the AEE estimator and the proposed method is thus more practical for real-world applications. Simulations and a case study demonstrate the satisfactory finite-sample performance of the proposed estimator. Keywords: Generalized linear models; Large-scale distributed data; Asymptotic efficiency; One-step MLE; Diverging p (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:157:y:2021:i:c:s0167947320302450 DOI: 10.1016/j.csda.2020.107154 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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