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Communication-efficient distributed estimator for generalized linear models with a diverging number of covariates

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  • Zhou, Ping
  • Yu, Zhen
  • Ma, Jingyi
  • Tian, Maozai
  • Fan, Ye

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.

Suggested Citation

  • Zhou, Ping & Yu, Zhen & Ma, Jingyi & Tian, Maozai & Fan, Ye, 2021. "Communication-efficient distributed estimator for generalized linear models with a diverging number of covariates," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    • Handle: RePEc:eee:csdana:v:157:y:2021:i:c:s0167947320302450
    DOI: 10.1016/j.csda.2020.107154
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    References listed on IDEAS

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