Penalized weighted composite quantile regression in the linear regression model with heavy-tailed autocorrelated errors

DOI10.1016/j.jkss.2014.03.004zbMath1304.62095OpenAlexW2037591816MaRDI QIDQ488598

Yunlu Jiang, Hong Li

Publication date: 26 January 2015

Published in: Journal of the Korean Statistical Society (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jkss.2014.03.004

zbMATH Keywords

composite quantile regressionoracle propertiesheavy-tailed autoregressive error models

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric robustness (62G35) Linear regression; mixed models (62J05)

Related Items (8)

Likelihood-based quantile autoregressive distributed lag models and its applications ⋮ Estimation of linear composite quantile regression using EM algorithm ⋮ Robust empirical likelihood for partially linear models via weighted composite quantile regression ⋮ Weighted composite quantile regression for longitudinal mixed effects models with application to AIDS studies ⋮ Bayesian LASSO-Regularized quantile regression for linear regression models with autoregressive errors ⋮ Bayesian bridge-randomized penalized quantile regression estimation for linear regression model with AP(q) perturbation ⋮ Quantile regression for linear models with autoregressive errors using EM algorithm ⋮ A robust and efficient estimation method for partially nonlinear models via a new MM algorithm

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