First-Order Newton-Type Estimator for Distributed Estimation and Inference

Xi Chen, Weidong Liu & Yichen Zhang
This article studies distributed estimation and inference for a general statistical problem with a convex loss that could be nondifferentiable. For the purpose of efficient computation, we restrict ourselves to stochastic first-order optimization, which enjoys low per-iteration complexity. To motivate the proposed method, we first investigate the theoretical properties of a straightforward divide-and-conquer stochastic gradient descent approach. Our theory shows that there is a restriction on the number of machines and this restriction becomes more...
1 citation reported since publication in 2021.
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