Quantile regression in big data: a divide and conquer based strategy

DOI10.1016/J.CSDA.2019.106892zbMath1504.62048OpenAlexW2990866518WikidataQ126648037 ScholiaQ126648037MaRDI QIDQ2291332

Yong Zhou, Lanjue Chen

Publication date: 30 January 2020

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2019.106892

zbMATH Keywords

quantile regressionmassive data setsestimating equationdata streamdivide and conquer

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Statistical aspects of big data and data science (62R07)

Related Items (9)

A review of distributed statistical inference ⋮ Robust distributed estimation and variable selection for massive datasets via rank regression ⋮ Divide and conquer for accelerated failure time model with massive time‐to‐event data ⋮ Optimal subsampling for large‐sample quantile regression with massive data ⋮ Distributed smoothed rank regression with heterogeneous errors for massive data ⋮ Optimal subsampling algorithms for composite quantile regression in massive data ⋮ Distributed statistical optimization for non-randomly stored big data with application to penalized learning ⋮ Robust communication-efficient distributed composite quantile regression and variable selection for massive data ⋮ Residual projection for quantile regression in vertically partitioned big data

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