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

DOI10.1080/01621459.2021.1891925zbMath1515.62051arXiv1811.11368MaRDI QIDQ6110706

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Publication date: 6 July 2023

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1811.11368

zbMATH Keywords

quantile regressionstochastic gradient descentdivide-and-conquerdistributed inferencenonsmooth loss

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonconvex programming, global optimization (90C26) Stochastic programming (90C15) Models and methods for concurrent and distributed computing (process algebras, bisimulation, transition nets, etc.) (68Q85)

Related Items (9)

A review of distributed statistical inference ⋮ Discussion on ‘A review of distributed statistical inference’ ⋮ Rejoinder on ‘A review of distributed statistical inference’ ⋮ Communication-Efficient Distributed Linear Discriminant Analysis for Binary Classification ⋮ Distributed estimation with empirical likelihood ⋮ Optimal subsampling for large‐sample quantile regression with massive data ⋮ Global debiased DC estimations for biased estimators via pro forma regression ⋮ Distributed Estimation for Principal Component Analysis: An Enlarged Eigenspace Analysis ⋮ Communication-Efficient Accurate Statistical Estimation

Cites Work

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