Shrinkage estimation for linear regression with ARMA errors

DOI10.1016/j.jspi.2012.02.047zbMath1237.62087OpenAlexW2045739137MaRDI QIDQ419339

Rongning Wu, Qin Wang

Publication date: 18 May 2012

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2012.02.047

zbMATH Keywords

variable selectionoracle estimatormodified lasso

Mathematics Subject Classification ID

Related Items (9)

Likelihood-based quantile autoregressive distributed lag models and its applications ⋮ Penalised inference for lagged dependent regression in the presence of autocorrelated residuals ⋮ Oracle model selection for correlated data via residuals ⋮ Bayesian LASSO-Regularized quantile regression for linear regression models with autoregressive errors ⋮ Model selection for time series with nonlinear trend ⋮ Bayesian bridge-randomized penalized quantile regression estimation for linear regression model with AP(q) perturbation ⋮ On estimation of nonparametric regression models with autoregressive and moving average errors ⋮ Quantile regression for linear models with autoregressive errors using EM algorithm ⋮ Asymptotic normality of Huber-Dutter estimators in a linear EV model with AR(1) processes

Cites Work

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• Least absolute deviation estimation for regression with ARMA errors
• Asymptotics for Lasso-type estimators.
• \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
• On the ``degrees of freedom of the lasso
• Regression Models with Time Series Errors
• Regression coefficient and autoregressive order shrinkage and selection via the lasso
• Least squares estimation in the regression model with autoregressive-moving average errors

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