Estimation of linear composite quantile regression using EM algorithm

DOI10.1016/J.SPL.2016.05.019zbMath1398.62184OpenAlexW2410962349MaRDI QIDQ310670

Qian-Qian Zhu, Mao-Zai Tian, Yu-Zhu Tian

Publication date: 8 September 2016

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2016.05.019

zbMATH Keywords

EM algorithmBayesian information criterionAkaike's information criterioncomposite quantile regressionCALD

Mathematics Subject Classification ID

Linear regression; mixed models (62J05)

Related Items (10)

An efficient estimation for the parameter in additive partially linear models with missing covariates ⋮ Single-index composite quantile regression for ultra-high-dimensional data ⋮ Two-layer EM algorithm for ALD mixture regression models: a new solution to composite quantile regression ⋮ Weighted composite quantile regression for longitudinal mixed effects models with application to AIDS studies ⋮ Composite quantile regression for massive datasets ⋮ Optimal subsampling algorithms for composite quantile regression in massive data ⋮ Single-index composite quantile regression for massive data ⋮ Weighted composite quantile regression for partially linear varying coefficient models ⋮ Robust communication-efficient distributed composite quantile regression and variable selection for massive data ⋮ Adaptive quantile regressions for massive datasets

Uses Software

• QRM
• bootstrap

Cites Work

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