Composite quantile regression for massive datasets

DOI10.1080/02331888.2018.1500579zbMath1411.62202OpenAlexW2885068887MaRDI QIDQ4580023

Rong Jiang, Xueping Hu, Wei-Min Qian, Ke-ming Yu

Publication date: 13 August 2018

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://bura.brunel.ac.uk/handle/2438/17163

zbMATH Keywords

variable selectioncomposite quantile regressionadaptive Lassodivide and conquermassive datasetmodel selection sensitivitymodel selection specificity

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items

Composite quasi-likelihood for single-index models with massive datasets, Optimal subsampling for composite quantile regression model in massive data, Bayesian joint-quantile regression, Distributed Sparse Composite Quantile Regression in Ultrahigh Dimensions, Distributed penalized modal regression for massive data, Communication-efficient distributed estimation of partially linear additive models for large-scale data, Optimal subsampling algorithms for composite quantile regression in massive data, Communication-efficient surrogate quantile regression for non-randomly distributed system, Communication-efficient sparse composite quantile regression for distributed data, Robust communication-efficient distributed composite quantile regression and variable selection for massive data, Adaptive quantile regressions for massive datasets, Optimal subsampling for composite quantile regression in big data

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