Published on Fri Jan 17 2020

Communication-Efficient Distributed Estimator for Generalized Linear Models with a Diverging Number of Covariates

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

A novel method is proposed to obtain an asymptotically efficient estimator for large-scale distributed data sets. In this novel method, the assumption on the number of servers is more relaxed and thus practical for real-world applications.

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Abstract

Distributed statistical inference has recently attracted immense attention. The asymptotic efficiency of the maximum likelihood estimator (MLE), the one-step MLE, and the aggregated estimating equation estimator are established for generalized linear models under the "large $n$, diverging $p_n$" framework, where the dimension of the covariates $p_n$ grows to infinity at a polynomial rate $o(n^\alpha)$ for some $0<\alpha<1$. Then a novel method is proposed to obtain an asymptotically efficient estimator for large-scale distributed data by two rounds of communication. In this novel method, the assumption on the number of servers is more relaxed and thus practical for real-world applications. Simulations and a case study demonstrate the satisfactory finite-sample performance of the proposed estimators.